mineral exploration machine learning

aprendizagem de máquina de exploração mineral

Esta página lista recursos para exploração mineral e aprendizado de máquina, geralmente com códigos e exemplos úteis. ML e ciência de dados são um campo enorme, são recursos que considero úteis e/ou interessantes para mim na prática. Os links atualmente para uma bifurcação de um repositório são porque mudei algo para usar e coloquei em uma lista para referência. Também são fornecidos recursos para análise, transformação e visualização de dados, pois essa é a maior parte do trabalho.

Sugestões são bem-vindas: abra uma discussão, problema ou pull request.

Índice

• Prospectividade
• Geologia
• Processamento de Linguagem Natural
• Sensoriamento Remoto
• Qualidade de dados
• Comunidade
• Provedores de nuvem
• Domínios
• Visão geral
• Serviços Web
• Portais de dados
• Ferramentas
• Ontologias
• Livros
• Conjuntos de dados
• Artigos
• Outro
• Interesse Geral

Mapa

• Mapa Github

Estruturas

• Estrutura UNCOVER-ML
  • Geo-Wavelets
  • Pré-processamento de ML
  • Fluxo de trabalho GIS ML
• EIS Toolkit -> Biblioteca Python para mapeamento de prospectividade mineral do Projeto EIS Horizon EU
• PySpatialML -> Biblioteca que facilita a previsão e manipulação de aprendizado de máquina raster automaticamente para geotiff, etc.
• mapa scikit
• TorchGeo -> Biblioteca Pytorch para modelos de estilo de sensoriamento remoto
• terratorch -> Estrutura flexível de ajuste fino para modelos de fundação geoespacial
• Tocha Espacial
  • papel
• geodl
• Geo Deep Learning -> Estrutura simples de aprendizado profundo baseada em RGB
• AIDE: Inteligência Artificial para Desembaraçar Extremos
• ExPloRA -> ExPLoRA: Pré-treinamento estendido com eficiência de parâmetros para adaptar transformadores de visão sob mudanças de domínio
  • Site
  • papel
• (https://www.researchgate.net/profile/Miguel-Angel-Fernandez-Torres/publication/381917888_The_AIDE_Toolbox_Artificial_intelligence_for_d isentangling_extreme_events/links/66846648714e0b03153f38ae/The-AIDE-Toolbox-Artificial-intelligence-for-disentangling-extreme-events.pdf)

R

• CAST -> Aplicações de cursor para modelos espaço-temporais
• geodl -> segmentação semântica de dados geoespaciais usando aprendizagem profunda baseada em rede neural convolucional
  • papel

Gasodutos

• geotargts -> Extensão de alvos para terra e estrelas

Prospectividade

Austrália

• Mapas de potencial mineral de cobre-ouro de óxido de ferro
• Aprendizado de máquina para mapeamento geológico: algoritmos e aplicações -> Tese de doutorado com código e dados
• Mapeamento de Prospectividade de Lateritas Ni-Co
• Tutorial Transform 2022 -> Exemplo de floresta aleatória
  • Vídeo
• Estanho-Tungstênio
  • Colaborar
• Exploração Espaço-Temporal de Cobre Pórfiro
  • papel
• minpot-toolkit -> Exemplo de análise de limite de laboratório de Hoggard et al com cobre sedimentar
• MPM-WofE -> Mapeamento de Potencial Mineral - Pesos de Evidência

Desafio do Explorador

• Explorer Challenge -> OZ Minerals realiza competição com introdução à ciência de dados

Sul da Austrália

• Gawler_MPM -> Cobalto, Cromo, Níquel
  • Papel
• Clustering de dados geofísicos no Cráton Gawler
  • [Zenodo Data](Detecção automatizada de estruturas de crátons relacionadas à mineralização usando dados geofísicos e aprendizado de máquina não supervisionado)

Explore SA - Competição do Departamento de Energia e Mineração da Austrália do Sul

• Vencedores -> informações de dados SARIG
• Caldera -> Análise Caldera Analytics
• IncertoData
• Butterworth e Barnett -> entrada de Butterworth e Barnett
• Mapeamento de mineralização baseado em dados

América do Norte

Canadá

• Aprendizagem de Prospectividade de Transferência
  • artigo -> Mapeamento de prospectividade mineral do tipo Porfírio com dados desequilibrados via aprendizagem de transferência geológica prévia

Ámérica do Sul

• Aprendizado de máquina para classificar depósitos de minério a partir de propriedades tectonomagmáticas

Brasil

• Mapa Preditivo -> Projeto estudantil Brasil
• Course_Predictive_Mapping_USP -> Projeto de Curso
• Mapeamento de Prospectividade Mineral
• Pesos de evidências 3D
• Complexidade Geológica SMOTE -> inclui análise fractal
  • papel
• MPM Jurena -> Província Mineral de Jurena

China

• MPM por aprendizagem em conjunto -> Distrito polimetálico Qingchengzi Pb-Zn-Ag-Au China
• Redes Neurais Convolucionais de Predição de Prospectividade Mineral -> Exemplo CNN com algumas arquiteturas [um artigo deste autor usa GoogleNet]
• Predição de Prospectividade Mineral por CSAE
• Previsão de Prospectividade Mineral por CAE
  • papel

Sudão

• Mapeamento de Prospectividade Mineral ML

Noruega

• Uma abordagem baseada em aprendizado de máquina para mapeamento em escala regional de argila glaciomarina sensível, combinando eletromagnetismo aéreo e dados geotécnicos
  • papel

Geologia

• Mapas de Geologia Preditiva do Brasil -> Trabalho do Serviço Geológico do Brasil
• profundidade até o leito rochoso (Avaliando abordagens de aprendizado de máquina habilitadas espacialmente para mapeamento de profundidade até o leito rochoso)
  • papel
• DL-RMD -> Um banco de dados de modelos de resistividade eletromagnética geofisicamente restritos para aplicações de aprendizagem profunda
• Classificador de imagens geológicas
• Mapeamento geológico na era da inteligência artificial -> Mapeamento geológico na era da inteligência artificial
• GeolNR -> Representação Neural Geológica Implícita para aplicações de modelagem geológica estrutural tridimensional
  • papel
• mapeamento_litológico_preditivo
• Mapeando as condições globais de pressão-temperatura do manto litosférico por termobarometria de aprendizado de máquina
  • papel
• Digitação de rocha neural
• Incerteza geológica de West Musgraves -> Previsão do mapa de incerteza com análise de entropia: altamente útil
• Transformador de mitigação não estacionário
  • Colaborar -> Caderno
    • papel
• Base rochosa versus sedimento
  • papel
• autoencoders_remotesensing
  • artigo -> Estrutura de sensoriamento remoto para mapeamento geológico via autoencoders empilhados e clustering

Dados de treinamento

• Into the Noddyverse -> um enorme armazenamento de dados de modelos geológicos 3D para aprendizado de máquina e aplicações de inversão
  • Repositório Zenondo
  • site

Litologia

• Litologia de Aprendizagem Profunda
• Preditor de Protólito de Rocha
• Previsões de Litologia de Geologia SA
• Correlação automatizada de registros de poços
• dawson-facies-2022 -> Transferência de aprendizagem para imagens geológicas
  • artigo -> Impacto do tamanho do conjunto de dados e da arquitetura da rede neural convolucional na aprendizagem por transferência para classificação de rochas carbonáticas
• Classificação Lito -> Classificação de fácies vulcânicas usando Floresta Aleatória
• Abordagem de aprendizado de máquina de conjunto multivisualização para modelagem 3D usando dados geológicos e geofísicos
  • papel
• SedNet
  • papel

Perfuração

• Perfuração Heterogênea - Relatório do projeto Nicta/Data61 para análise de modelagem usando furos de sondagem que não vão longe o suficiente
• corel -> modelo inteligente de visão computacional que identifica fácies e realiza digitação de rochas em imagens centrais

Paleovales

• Sub3DNet1.0: um modelo de aprendizagem profunda para mapeamento de estruturas subterrâneas 3D em escala regional

Estratigrafia

• Predicatops -> Predicação estratigráfica projetada para hidrocarbonetos
• stratal-geometries -> Previsão de geometrias estratigráficas a partir de registros de poços subterrâneos

Estrutural

• APGS -> Pacote de geologia estrutural
• Avaliação de modelos de reconstrução de placas usando testes de consistência de força motriz de placas -> Caderno e dados Jupyter
  • papel
• gplately
• [livro de receitas de geologia estrutural](https://github.com/gcmatos/structural-geology-cookbook]
• GEOMAPLEARN 1.0 -> Detectando estruturas geológicas a partir de mapas geológicos com aprendizado de máquina
• Aprendizado de Lineamento -> Previsão e mapeamento de falhas por meio de aprendizado profundo de campo potencial e clustering
• LitMod3D -> Modelagem interativa geofísica-petrológica integrada em 3D da litosfera e manto superior subjacente
• outros

Simulação

• GebPy -> geração de dados geológicos de rochas e minerais
• OpenGeoSys -> desenvolvimento de métodos numéricos para simulação de processos termo-hidro-mecânico-químicos (THMC) em meios porosos e fraturados
• Stratigraphics.jl -> Criação de estratigrafia 3D a partir de processos geoestatísticos 2D

Geodinâmica

• Badlands -> Dinâmica de bacias e paisagens
• CitcomS -> código de elementos finitos projetado para resolver problemas de convecção termoquímica compressível relevantes para o manto terrestre.
• LaMEM -> simula vários processos geodinâmicos termomecânicos, como a interação manto-litosfera
• PTatin3D -> estudar processos de longa escala relevantes para a geodinâmica [motivação original: kit de ferramentas capaz de estudar modelos tridimensionais de alta resolução de deformação litosférica]
• submundo -> Modelagem de elementos finitos da geodinâmica

Geofísica

Modelos de Fundação

• Adaptação de modelo de base entre domínios: modelos pioneiros de visão computacional para análise de dados geofísicos -> alguns dos códigos que virão
  • papel
• Modelo de Fundação Sísmica -> "um modelo de aprendizagem profunda de nova geração em geofísica"

Austrália

Profundidade do Regolito

• Profundidade do Regolito -> Modelo
• Grade radiométrica completa da Austrália com preenchimento modelado

Interpolação AEM

• Mapeamento de condutividade de alta resolução usando pesquisa regional AEM
  • Resumo

Eletromagnetismo

• TEM-NLnet: Uma rede de eliminação profunda de ruído para sinais eletromagnéticos transitórios com aprendizagem de ruído

Inversão

• Aprendizado de máquina e inversão geofísica -> artigo reconstruído de Y. Kim e N. Nakata (The Leading Edge, Volume 37, Edição 12, dezembro de 2018)

Deconvolução de Euler

• https://legacy.fatiando.org/gallery/gravmag/euler_moving_window.html
  • Versão harmônica eventualmente? https://hackmd.io/@fatiando/development-calls-2024?utm_source=preview-mode&utm_medium=rec
• https://notebook.community/joferkington/tutorials/1404_Euler_deconvolution/euler-deconvolution-examples
• https://github.com/ffigura/Euler-deconvolution-plateau

Gravidade

• [Recuperando relevo 3D do porão usando dados gravitacionais por meio de redes neurais convolucionais]
  • Dados
  • papel
• Continuação descendente estável do campo potencial gravitacional implementado usando aprendizado profundo
  • papel
• Imagens rápidas para estruturas de densidade 3D por abordagem de aprendizado de máquina
  • papel

Magnética

• Mapa aeromagnético de alta resolução através do Adapted-SRGAN
  • papel
• MagImage2Geo3D
  • papel

Sísmico

• StorSeismic -> Uma abordagem para pré-treinar uma rede neural para armazenar recursos de dados sísmicos
• PINNtomo -> Tomografia sísmica usando redes neurais informadas pela física
  • papel

Sismologia

• obspy -> framework para processamento sismológico

Petrofísica

• ML4Rocks -> Alguns trabalhos de introdução

Tectônica

• Discernir o descolamento da laje de subducção em uma antiga zona de subducção usando aprendizado de máquina -> Notebook
  • figo
• Caderno Colab -> arquivo de entrada do Google Colab para resultados de benchmark da publicação ML-SEISMIC
  • papel
• Liberando o poder do aprendizado de máquina em geodinâmica
  • Tese de Honra relacionada
• Redes Neurais com Informações Físicas para simulação de deslizamento de falhas com taxa e lei de atrito de estado
  • simulação e estimativa de parâmetros de atrito em eventos de escorregamento lento
  • artigo -> Aprendizado profundo informado pela física para estimar a distribuição espacial de parâmetros de atrito em regiões de deslizamento lento

Geoquímica

• CODAinPractice -> Análise de dados composicionais na prática
• GeoCoDa
• DAN-GRF -> Rede profunda de autoencoder conectada a floresta geográfica aleatória para detecção de anomalias geoquímicas com consciência espacial
  • papel
• Dash Geochemical Prospection -> Web-app classificando sedimentos de riachos com K-means
• Aprimorando a termobarometria de aprendizado de máquina para magmas contendo clinopiroxênio
  • artigo -> Melhorando-ML-Termobarometria-para-Magmas de Rolamento de Clinopiroxênio
• Modelos de fertilidade de zircão -> Árvores de decisão para prever zircão fértil a partir de depósitos de cobre pórfiro
  • papel
• Ferramenta de oligoelementos de zircão de aprendizado de máquina para prever o tipo de depósito de pórfiro e o tamanho do recurso
  • arquivo zenodo
  • papel
• geology_class0 -> Uma abordagem de aprendizado de máquina para discriminação de rochas ígneas e depósitos de minério por oligoelementos de zircão
  • papel
  • Aplicativo de demonstração
  • https://colab.research.google.com/drive/1-bOZgG6Nxt2Rp1ueO1SYmzIqCRiyyYcT
• GeoquímicaPrint
  • papel
• Geoquímica global
• ICBMS Jacobina -> Análise química da pirita em jazida de ouro
• Interpretação da Química de Oligoelementos de Zircões de Bor e Cukaru Peki: Abordagem Convencional e Classificação Florestal Aleatória
• Indicator_minerals -> O PCA pode contar a história da origem da turmalina?
• Jornal de Exploração Geoquímica - Manifold
• LewisML -> Análise da Formação Lewis
• MICA -> Composição química, em Brilhante
• Análise estatística multivariada e modelagem de rede de desvio sob medida para detecção de anomalias geoquímicas de elementos de terras raras
  • papel
• Mapeamento prospectivo de elementos de terras raras através de análise de dados geoquímicos -> Mapeamento prospectivo de elementos de terras raras através de análise de dados geoquímicos
• QMineral Modeller -> Assistente virtual de Química Mineral do Serviço Geológico Brasileiro
• Mudanças seculares na ocorrência de subducção durante o Arqueano -> arquivo de código Zenodo
  • [artigo] https://www.researchgate.net/publication/380289934_Secular_Changes_in_the_Occurrence_of_Subduction_During_the_ArcheanUma abordagem de aprendizado de máquina para discriminação de rochas ígneas e depósitos de minério por oligoelementos de zircão

Krigagem

• DKNN: rede neural de krigagem profunda para interpolação geoespacial interpretável
  • papel

Processamento de Linguagem Natural

• Extração de texto -> Extração de texto de documentos: ML pago como serviço, mas funciona muito bem, pode extrair tabelas com eficiência
  • Grande Escala -> Versão em grande escala
• Marcação de conceito da NASA -> Previsão de palavras-chave
  • API -> serviço web API
  • Apresentação
• Extrator de dados de relatório de petrografia
• Modelagem de tópicos de exploração SA -> Modelagem de tópicos de relatórios de exploração
• Estratígrafo
• Geocorpus
• BERT Português
• BERT CWS
  • papel
• Extração automatizada de resultados de furos de sondagem de empresas de mineração
  • Documento de Conferência

Incorporações de palavras

• Modelos de linguagem de geociências -> pipeline de processamento de código e modelos [Glove, BERT) treinados novamente em documentos de geociências do Canadá
  • Conjuntos de dados -> Dados para suportar modelos
  • artigo -> Modelos de linguagem em geociências e sua avaliação intrínseca
  • artigo -> Aplicações de Processamento de Linguagem Natural a Dados de Texto de Geociências e Modelagem de Prospectividade
• GeoVec -> Modelo de incorporação de palavras treinado em 300 mil artigos de geociências
  • Modelo GeoVec -> Armazenamento OSF para modelo GeoVec
  • papel
  • GeoVecto Litho -> Interpolação de modelos 3D a partir de embeddings de palavras
  • GeoVEC Playground -> Trabalhando com o modelo de embeddings de palavras de luva Padarian GeoVec
• GloVe -> Biblioteca Standford para produção de embeddings de palavras
  • gloVE python glove, glove-python altamente problemático no Windows: aqui a versão binária para Windows é instalada:
• Mittens -> Implementação de luva vetorizada na memória
• PetroVec -> Embeddings de palavras em português para a indústria de petróleo e gás: desenvolvimento e avaliação
• wordembeddingsOG -> Embeddings de palavras de Petróleo e Gás Português
• Incorporações de palavras em português
  • Modelos portugueses
• Incorporações de palavras em espanhol
• Alinhamento multilíngue
  • Visão geral das abordagens

Reconhecimento de Entidade Nomeada

• Modelo Geo NER -> Reconhecimento de entidade nomeada
• GeoBERT - repositório de abraços faciais para modelo em
  • [artigo] https://www.researchgate.net/publication/359186219_Few-shot_learning_for_name_entity_recognition_in_geological_text_based_on_GeoBERT
• INDUS -> Conjunto LLM sob medida para ciência da NASA
  • papel
• Como encontrar os principais termos de geociências no texto sem dominar a PNL usando o Amazon Comprehend
• OzRock - OzRock: um conjunto de dados rotulado para reconhecimento de entidades no domínio geológico (exploração mineral)

Ontologia

• GAKG -> Um gráfico de conhecimento acadêmico de geociências multimodais (chinês)
• GeoERE-Net -> Compreender relatórios geológicos baseados em gráficos de conhecimento usando uma abordagem de aprendizagem profunda
  • papel
• Ontologia GeoFault
• geosim -> Simulação qualitativa acionada semanticamente de um processo geológico
  • [https://www.duo.uio.no/handle/10852/111467](Modelagem de Conhecimento para Geologia Digital) -> Tese de doutorado com dois artigos
  • SIRIUS GeoAnnotator -> Exemplo de site acima
• Ontologia CWS
• Gráfico de conhecimento estratigráfico (StraKG)
  • papel

Grandes modelos de linguagem

• Grande modelo de linguagem para geociências
  • Artigo Learning Foundation Language Models for Geoscience Knowledge Understanding and Utilization
• GeoGalactica -> Um modelo de linguagem de base mais amplo em Geociências
  • relatório técnico
• GeoChat -> Modelo de Linguagem de Visão Grande para Sensoriamento Remoto
• LAGDAL -> LLM Combinando informações de mapas geológicos com experimentos de localização

Bots de bate-papo

• GeoGPT -> Projeto Deep Time Digital Earth Research Group da China

Sensoriamento Remoto

• CNN Sentinel -> Visão geral sobre a classificação do uso do solo a partir de dados de satélite com CNNs com base em um conjunto de dados aberto
• Notebooks DEA -> Exemplo de aprendizado de máquina escalonável, mas muitas coisas úteis aqui
• Cadernos de receitas EASI -> introduções da plataforma CSIRO Earth Analytics para análise de estilo ODC
• DS_UNet -> Unet fundindo o Radar de Abertura Sintética (SAR) Sentinel-1 e o Imageador Multiespectral Sentinel-2
• Autoencoder mascarado com vários pretextos (MP-MAE)
• dados
• segment-geospatial -> Segmente qualquer coisa para usos geoespaciais
• SamGIS -> Segmentar qualquer coisa aplicada ao GIS
• SatMAE++ -> Repensando o pré-treinamento de transformadores para imagens de satélite multiespectrais
• grid-mae -> Investigue usando grades multiescala em um Vision Transformer Masked Autoencoder
• EscalaMae
  • papel
• CIMAE -> CIMAE - Autoencoder mascarado independente de canal
• fork -> para dar um nome para referência
• [Aprendizagem de Representação Auto-Supervisionada para Sensoriamento Remoto] -> A dissertação de mestrado inclui o acima e comparações de vários modelos
• Celeiro
  • papel
• redes terrestres
• GeoTorchAI -> GeoTorchAI: uma estrutura de aprendizado profundo espaçotemporal
• [pytorcheo](https://github.com/earthpulse/pytorchEO -> Deep Learning para aplicações e pesquisas de observação da Terra
• AiTLAS -> um conjunto de benchmark de código aberto para avaliar abordagens de aprendizagem profunda de última geração para classificação de imagens em Observação da Terra
• Segmentation Gym -> Gym foi projetado para ser um "balcão único" para segmentação de imagens em "ND" - qualquer número de bandas coincidentes em uma imagem multiespectral
• deep_learning_alteration_zones
• incrível coleção de proporção de banda de mineração -> coleção de usos simples de proporção de banda para destacar vários minerais
• incríveis modelos de base de sensoriamento remoto
• Clay -> Um modelo de IA de código aberto e interface para a Terra
• IBM-NASA-GEOSPATIAL Prithvi
• Segmentação de imagem por ajuste fino do modelo de base -> Para Prithvi
• AM-RADIO: Modelo de Fundação de Visão Aglomerativa
  • papel -> - Reduzir todos os domínios em um
• RemoteCLIP -> Um modelo Vision Language Foundation para sensoriamento remoto
• EspectralGPT
• zenodo) -> modelo básico de sensoriamento remoto personalizado para dados espectrais
  • papel -> Invisível

Processamento

• Conversão ASTER -> Conversão de ASTER hd5 para geotiff NASA github
• Recursos de dados HLS -> Disputas harmonizadas do Landsat Sentinel
• sarsen -> processamento e correção de imagem SAR baseado em xarray
• openEO -> openEO desenvolve uma API aberta para conectar R, Python, JavaScript e outros clientes a back-ends de nuvem EO

Desmistura Espectral

• Pesquisa de classificação de imagem convencional para transformador para classificação de imagem hiperespectral-2024
  • papel
• Revisão de aprendizagem profunda hiperespectral
• Autoencodificadores hiperespectrais
• Aprenda profundamente em HSI
• Classificação hiperespectral 3DCAE
• DeHIC
• Rev-Net
  • artigo -> Uma rede generativa reversível para separação hiperespectral com variabilidade espectral
• Pysptools -> também possui algoritmos heurísticos úteis
• Python Espectral
• Conjunto de dados espectrais RockSL -> Conjunto de dados espectrais abertos
• Desmisturando

Hiperespectral

• CasFormer: Transformadores em cascata para imagens hiperespectrais computacionais com reconhecimento de fusão
• Normalização espectral para Keras
  • papel
• S ^ 2HM ^ 2 -> S2HM2: uma estrutura de modelagem hierárquica espectral-espacial mascarada para aprendizado de recursos autosupervisionados e classificação de imagens hiperespectrais em grande escala
  • papel

Visualização

• Extração profunda de mapas de cores de visualizações
  • papel
• Segmentação semântica para extração de perturbações históricas de mineração superficial de mapas topográficos -> O exemplo é para minas de carvão
  • papel
• Códigos Cronoestratigráficos Internacionais de Cores -> Códigos RGB e outros em planilhas e outros formatos
• LithClass -> versão USGS dos códigos de cores da litologia
• versão colorida
• SeisWiz -> Visualizador SEG-Y python leve

Textura

• Classificação de textura mineral usando redes neurais convolucionais profundas: uma aplicação a zircões de depósitos de cobre pórfiro
  • papel

Simulação

• Prospector Inteligente -> Planejamento sequencial de aquisição de dados
• Zenodo
  • papel

Geometria

• Deep Angle -> Cálculo rápido de ângulos de contato em imagens tomográficas usando deep learning

Outro

• Análise de Rede de Sistemas Mineralógicos
  • Dados -> Dados do papel aqui
• Geoanalítica e aprendizado de máquina
• Subsuperfície de aprendizado de máquina
• Geociências de ML
• Seja um detetive de geociências
• Earth ML -> Alguns tutoriais básicos em abordagens PyData
• GeoMLA -> Algoritmos de aprendizado de máquina para dados espaciais e espaçotemporais

Plataformas

Guias

• CLI Geoespacial - Lista de ferramentas de linha de comando geoespaciais
• Aprendizado profundo de imagens de satélite
• Observação da Terra
• Inteligência Artificial da Terra
• GIS de código aberto -> Visão geral abrangente do ecossistema

Qualidade de dados

• Qualidade de dados de geociências para aprendizado de máquina -> Qualidade de dados de geociências para aprendizado de máquina
• Dados de gravidade australianos -> Visão geral e análise de dados da estação de gravidade
• Geodiff -> Comparação de dados vetoriais
• Redflag -> Análise de dados e visão geral para detectar problemas

Aprendizado de máquina

• Dask-ml -> Versões distribuídas de alguns algoritmos de ML comuns
  • papel
• geoespacial-rf -> Funções e wrappers para auxiliar em aplicações florestais aleatórias em um contexto espacial
• Geospatial-ml -> Instale vários pacotes comuns de uma vez

Espaço Latente

• Fusão aninhada
  • artigo -> Fusão aninhada: redução de dimensionalidade e análise de estrutura latente de dados aninhados multiescala para dados M2020 PIXL RGBU e XRF

Métricas

• pontuações -> Verificando e avaliando modelos e previsões com xarray

Probabilístico

• NG Boost -> regressão probabilística
• ML probabilístico
• Ensacamento PU com BO -> Ensacamento não rotulado positivo com otimização bayesiana

Agrupamento

Mapas auto-organizados

• GisSOM -> Mapas auto-organizados centrados geoespacialmente do Finland Geological Survey
• SimpSOM -> Mapas auto-organizados

Outro

• hdbscan
• kmedóides
  • kmedóides
• Picasso

Bayesiano

• Bayseg -> Segmentação espacial

Explicabilidade

• InterpretML -> Interpretando modelos de dados tabulares
• InterpretML -> Adição de comunidade

Aprendizado profundo

• Extração Profunda de Colormap -> Tentando extrair uma escala de dados de imagens
• Extraia e classifique imagens de documentos de geociências

Dados

• Xbatcher -> Leitura de dados baseada em Xarray para aprendizado profundo
  • Carregadores de dados nativos da nuvem para aprendizado de máquina usando Zarr e Xarray
• zen3geo -> Ciência de dados estilo Xbatcher com pytorch

Explicabilidade

• Valores de forma
• Vigilante do Peso -> Analise quão bem as redes são treinadas
• vigilante de peso.ai
• Weightwatcher-ai.com -> Versão web profissional

Aprendizagem auto-supervisionada

• Auto-supervisionado -> Implementações de relâmpagos Pytorch de vários algoritmos
• Simclr
• Aprendizado autossupervisionado incrível -> Lista selecionada

Hiperparâmetros

• Hiperóptico
• ML automatizado TPOT

Ambientes de codificação

• Caixa de areia DEA
• Cubo em uma caixa

Comunidade

• Software Underground - Comunidade de pessoas interessadas em explorar a interseção do subsolo e do código
  • Inscrição no bate-papo - inscrição no bate-papo da comunidade SWUNG
  • Mattermost- Serviço de bate-papo comunitário
    • Antigo canal do Slack (obsoleto, veja o assunto acima)
  • Associação de código aberto de geociências
  • Vídeos
    • Transformar 2022
  • Impressionante geociência aberta [nota tendenciosa para petróleo e gás]
  • Exemplos de hackers do Transform 2021
  • Tutorial Segysak 2021
  • Caderno Sísmico T21
  • Sísmica Prática com Python
  • Transformar 2021 Simpeg
• Pangeu
  • Fórum
  • Melhores práticas de COG
• Terra Digital Austrália
  • Canal Slack
• Fundação Geoespacial de Código Aberto
• OSGeoLive -> DVD/USB inicializável com muitos softwares geoespaciais de código aberto
• ASEG -> vídeos da Sociedade Australiana de Geocientistas de Exploração
• IA para modelagem e mapeamento geológico -> vídeos do dia da conferência
• conferência

Provedores de nuvem

AWS

• ec2 Spot Labs -> Facilitando o trabalho automático com instâncias Spot
• Sagemaker Geoespacial ML
• Sagemaker -> Serviço gerenciado de ML
  • SDK
  • Utilitários de ponto de entrada
  • Oficina 101
  • Kit de ferramentas de treinamento

Lote

• Shepard -> Configuração automatizada de formação de nuvem do AWS Batch Pipelines: isso é ótimo

Pacotes

• Mlmax - Iniciar biblioteca rápida
• Pequena matéria
• Pyutil

Em geral

• Contêineres de aprendizado profundo
• Loguru -> Biblioteca de registro
• Robô AWS GDAL -> Lambda e processamento em lote de geotiffs
• Processamento Sísmico Sem Servidor
• LIthops -> estrutura de computação distribuída multinuvem

Visão geral

• Exploração Mineral

Domínios

• Geologia
  • Eras Geológicas
  • Litologia
  • Estratigrafia
• Geoquímica
• Geofísica
• Sensoriamento Remoto

Serviços Web

Se listados, presume-se que geralmente são dados; se forem apenas imagens como WMS, isso será dito.

Mundo

• Minerais e Depósitos Críticos

Austrália

• AusGIN
• Geociências Austrália
• Potencial Mineral -> WMS
• Serviço de catálogo da Geoscience Australia

Geologia

• AUSLAMP -> Tennant Creek - MtIsa
• Geologia de Campo
• Litosfera Profunda -> Potencial Mineral Litosférico Profundo
• Geocronologia -> Geocronologia
• Províncias Geológicas
• WMS -> imagem WMS
• EGGS -> Estimativas de Superfícies Geológicas e Geofísicas
• Rochas Alcalinas Proterozóicas - Conjunto de dados de rochas alcalinas proterozóicas WFS {também tem WMS}
• Cenozóico
• Mesozóico
• Paleozóico
• Arqueano
• Estratigrafia -> Unidades Estratigráficas

Geofísica

• Pesquisas Geofísicas
• Pesquisas Sísmicas -> Pesquisas sísmicas em terra
• Magnetotelúrico -> Estações AUSLAMP do Norte da Austrália

Outro

• Ni-Cu-PEGE -> Depósitos PGE de Níquel Cobre Hospedados por Intrusão
• Área EFTF -> Explorando as áreas futuras
• Temperatura -> Temperatura interpretada
• DEA -> Digital Earth Austrália
• Cobertura do solo
• Corpos d'água
• BOM -> Bureau de Meteorologia Hidrogeoquímica

Nova Gales do Sul

• Novo estado do estado
• WCS
• Furos de sondagem mineral WFS
• Furos de perfuração de petróleo WFS
• Furos de perfuração de carvão WFS
• Sísmica -> Sísmica e outras

Queensland

• Queensland
• Geocientífico -> Geofísica e Índice de Relatórios
• Geologia
• Regional
• Estado
• Cortiços
• Estradas
• Curso de água

Sul da Austrália

• SARIG
• Furos
• Geologia
• Geofísica
• Prospectividade
• Minerais e Minas
• Sensoriamento Remoto
• Sísmico
• Cortiços

Território do Norte

• NTGS -> Pesquisa Geológica do Território do Norte

Tasmânia

• Tasmânia WFS
• RESTO DA Tasmânia
• Furos

Vitória

• Victoria Geonetwork

Austrália Ocidental

• Austrália Ocidental
• Descansar

Nova Zelândia

• GNS -> Lista de serviços web

Ámérica do Sul

Brasil

• Geoportal Brasil
• CPRM Brasil

Peru

• Processamento
• Ocorrências Minerais
• Ambiental

México

• GeoInfo -> Serviços de descanso

Argentina

• SIGAM

Colômbia

• Descansar
• Depósitos 2018

Uruguai

• Descansar

Outro

• SIG Andes -> Geologia dos Andes

Europa

EGDI -> Minerais EGDI

Suécia

• SGU Magnética WMS
• Urânio SGU
• Metadados geofísicos

Finlândia

• GTK -> Pesquisa Geológica da Finlândia
  • Finlândia
    • Geologia Rochosa
    • Geofísica
    • Levantamentos Terrestres
• Minerais do Ártico -> Ocorrências Minerais do Ártico 1M

Dinamarca

• deus -> Groenlândia WMS/WFS

Portugal

• Geologia de Portugal
• Ocorrências Minerais -> WMS
• Cidades e Vila

Espanha

• Espanha
• Geologia -> 200K
• 1 milhão -> 1 milhão
• 50K -> 50K
• Geodo IGME
• Geofísica
• Cobre - Cobre
• GeoFPI -> Geologia e Minerais Zona Sul de Portugal
• Água

Ucrânia

• Geoinform -> [atualmente suspenso]

Irlanda

• Descansar
• Locais Minerais

Grã-Bretanha

• BGS -> Serviço Geológico Britânico
• Geoíndice -> exemplo de ocorrência mineral
• Descanso -> Serviços BGS Rest e Inspire 625

Alemanha

• BRG

República Tcheca

• Descansar

Eslováquia

• Descansar

Hungria

• MBFZ OGC
• Descanso MBFZ

Romênia

• IGR -> somente WMS
• Minérios IGR -> somente WMS

Polônia

• Exemplo de descanso -> Muitos mais servidores de mapas

América do Norte

Canadá

• Quebeque
• TNM
• Descansar
• Referências

EUA

• Mineral Mundial USGS
• MRDS do USGS
• Minesota

Ásia

• China -> Wap de depósito mineral WMS
• campo mineiro -> Pontos de ocorrência mineral
• Índia Mineral -> WMS
• Geofísica da Índia

África

• Geoportal África -> Serviços de descanso
• África 10M -> Ocorrências Minerais da África 10M https://pubs.usgs.gov/of/2005/1294/e/OF05-1294-E.pdf
• IPIS Minas Artesanais -> Existe uma versão WMS também
• GitHub
• Uganda -> GMIS WMS

Em geral

• Serviços Web de Exploração Mineral -> Plugin QGIS com acesso a muitos serviços web relevantes

Outro

• Abra o mapa de ruas -> serviço geral útil de blocos

APIs

• API de dados abertos -> API do portal de dados abertos GSQ
  • Análise geoquímica
• CORE -> Textos de Pesquisa Abertos
  • API Notebook -> Exemplo e funções
• COMPARTILHE -> API Open Science
• Publicações do USGS
• CROSSREF
• xDD -> antigo GeoDeepDive
• ADEPT -> GUI para xDD para pesquisar 15 milhões de papéis colhidos
• OpenAlex
  • API
  • Biblioteca Python diophila
  • Biblioteca Python
    • papel
• Macrostrato
  • papel
• OpenMinData -> facilita a consulta e recuperação de dados sobre minerais e geomateriais da API Mindat

Portais de dados

Mundo

• Colaboração do modelo terrestre -> acesso a vários modelos terrestres, ferramentas de visualização para visualização do modelo, recursos para extrair dados/metadados do modelo e acesso ao software e scripts de processamento contribuídos.
• Boletim ISC -> Pesquisa de mecanismo focal de terremoto
• [Consórcio de Informações Magnéticas[(https://www2.earthref.org/MagIC/search) -> paleomagnético, geomagnético, rocha magnética

Austrália

Geociências Austrália

• Catálogo de dados da Geoscience Australia
• AusAEM
• Portal de Geociências da Austrália
• Portal Explorando para o Futuro -> Portal da Geoscience Australia com informações para download
• AusAEM
• AusLAMP
• Geocronologia e Isótopos
• Bacias Hidrogeológicas -> procurar camada de bacias hidrográficas
• Iniciativa de mapeamento de minerais críticos
• Unidades Estratigráficas Australianas
• Unidades estratigráficas de poços australianos -> Compilação de águas subterrâneas de unidades sedimentares
• Segmentos de geofísica da Geoscience Australia -> OpendDAP e acesso https
• MORPH gdb -> Dados de perfuração do oficial Musgrave

CSIRO

• Portal de acesso a dados CSIRO
• Profundidade do Regolito
• TWI -> Índice Topográfico de Umidade
• Mapas de geociências ASTER -> Site
  • FTP -> site FTP CSIRO
  • Notas do ASTER Maps -> Notas para o acima

AuScope

• Geologia 3D -> Modelos de múltiplas áreas

TERN

• Covariáveis ​​aprimoradas de terra descoberta para modelagem litológica e de solo

Departamento de Meteorologia

• Explorador de Águas Subterrâneas -> Departamento de Meteorologia

Dados Espaciais Fundamentais

• Elvis

Sul da Austrália

• SARIG -> Pesquisa baseada em mapa geoespacial do South Australia Geological Survey
• Catálogo SARIG -> catálogo de dados
  • Modelos 3D
    • Amplo SA
    • localização s3
  • Pacotes de dados – atualização anual
  • Relatórios s3 -> Relatórios e versões textratadas no bucket s3 com interface web)
  • Relatórios
  • Sísmico
    • Downloads sísmicos -> Uma página de links

Território do Norte

• STRIKE -> Pesquisa Geológica do Território do Norte
• GEMIS
  • Bacia McArthur -> Modelo 3D
  • Levantamentos Geofísicos
  • Geofísica -> referência
  • Perfuração e Geoquímica -> referência
    • Pacote de dados -> pacote de dados

Queensland

• Pesquisa Geológica de Queensland
• Levantamentos Geofísicos
• Perfuração e geoquímica

Austrália Ocidental

• GEOVIEW -> Pesquisa Geológica da Austrália Ocidental
• DMIRS -> Centro de Dados e Software DMIRS
• URLS de download -> conjunto de dados de links de download
• Perfuração e Geoquímica
• Baixar pacote - melhoria?
• Geoquímica
• Poços de petróleo com profundidades
• subconjunto de dados WA

Novo estado do estado

• MINVIEW -> Pesquisa Geológica de Nova Gales do Sul
• DiGS -> Publicações e coleções geotécnicas

Tasmânia

• MRT
• Mapas MRT -> Mapa da Web

Vitória

• Recursos Terrestres
  • Geofísica
• GeoVIC -> Webmaps precisa de registro para ser mais útil

Nova Zelândia

• Banco de dados de exploração -> Online
• GERM -> Mapa de recursos geológicos da Nova Zelândia
• Geologia -> Mapa da Web
  • https://maps.gns.cri.nz/gns/wfs

Ámérica do Sul

Brasil

• CPRM -> Serviço Geológico Brasil
• Downloads -> Downloads do Pesquisa Geológica do Brasil
• Rigeo -> Repositório Institucional de Geociências

Peru

• Ingemmet GeoPROMINE -> Levantamento Geológico do Peru
• GeoMAPE

México

• Informações geográficas

Argentina

• SIGAM -> Levantamento Geológico da Argentina
• SIGAM

Colômbia

• Geportal

Uruguai

• Imagem

Chile

• Portalgeomin

Europa

• EGDI -> Geociências da Europa
• WFS
• Promete
• Inspirar -> Inspirar Geoportal

Dinamarca

• Dados de subsuperfície dinamarqueses

Finlândia

• Minerais4EU
• GTK -> Pesquisa Geológica da Finlândia
• Mapas Geoquímicos -> somente pdf!

Suécia

• SGU -> Pesquisa Geológica Sueca

Espanha

• IGME -> Serviço Geológico Espanhol

Portugal

• Geoportal
• Ocorrências Minerais

Irlanda

• GSI -> Pesquisa Geológica da Irlanda
• GSI - Visualizador de mapa
• Goldmine -> Pesquisa de mapas e documentos
• data.gov.ie -> Visualização do portal nacional
• isde -> Troca de dados espaciais irlandeses

Noruega

• NGU -> Pesquisa Geológica da Noruega
• banco de dados -> recursos minerais e pesquisas de estratigrafia
• GitHub
• API
• Geoporta -> Geofísica
• GEONORGE -> Catálogo de dados com download

Grã-Bretanha

• Grã-Bretanha
• servidor de mapas
• GitHub

Ucrânia

• Recursos minerais

Rússia

• Instituto Russo de Pesquisa Geológica -> Inacessível atualmente
• RGU -> Projeto GIS de depósitos

Alemanha

• Geoportal
• Geomapa -> M
• Atom -> Feed de dados Atom
• GDI -> Modelos 3D Alemanha

França

• Infoterre -> Serviço Geológico Francês

Croácia

• Geoportal -> Pesquisa Geológica da Croácia
• Geologia

República Tcheca

• GS -> Pesquisa Geológica Tcheca

Eslovênia

• Catálogo de Dados

Eslováquia

• Catálogo de Dados
• API Geoportapi

Hungria

• Mapas MBFSZ

Romênia

• IGR -> Pesquisa Geológica da Romênia
• Recursos minerais

Polônia

• Geoportal

Reino Unido

• Biblioteca Geofísica Onshore do Reino Unido
• OS Data Hub Geologia Britânica
• Geologia 625

América do Norte

Canadá

• Recursos Naturais Canadá
• GitHub
• Repositório de dados de geociências -> Servidor DAP
• Portal de mapas de mineração
• DEM -> Canadá DEM em formato COG
• CDEM -> Modelo Digital de Elevação (2011)
• Ontário
• Quebeque
• Banco de dados SIGEOM
• Colúmbia Britânica
• Banco de dados de ocorrência mineral
• Yukon
  • FTP
• Nova Escócia
• provincial
• Ilha do Príncipe Eduardo
• Saskatchewan
• Banco de dados de ocorrência mineral
• Newfoundland -> não funcionou no Chrome, tentei no Edge
• Alberta
• Aplicativo de mapeamento interativo
• Territórios do Noroeste
• Posse Mineral

EUA

• USGS -> Banco de dados de mapas
• MRDS -> Sistemas de Dados de Recursos Minerais
• Earth Explorer -> Portal de dados de sensoriamento remoto do USGS
• Banco de dados de mapas nacionais
• Banco de dados de mapas nacionais
• Alasca
• ReSci -> Cadastro de Coleções Científicas do Programa Nacional de Preservação de Dados Geológicos e Geofísicos
• Michigan

África

• Cadastro
• Hidrogeologia -> Hidrogeologia e geologia do atlas de águas subterrâneas
• África Ocidental -> Depósitos minerais
• Namíbia
• Ocorrências Minerais
• Mineiros
• África do Sul -> Pesquisa geológica da África do Sul
• Ocorrências Minerais -> Exemplo onde você precisa fazer login para fazer o download
• Uganda -> portal GMIS
• Minerais metálicos
• Tanzânia
• Ocorrências Minerais
• Minas
• SIGM -> Tunísia Geologia e Mineração
• Zâmbia -> Cortiços aqui na Zâmbia

Ásia

China

• Dados geocientíficos
• Ocorrências Minerais
• Banco de Dados Nacional de Depósitos Minerais

Índia

• Bhukosh -> Pesquisa Geológica da Índia
• Nota: a geologia do Rajastão não funciona, exceto aos poucos, o que é doloroso - se você quiser, me avise

Arábia Saudita

• Portal de banco de dados geológico nacional

Outro

Geologia

• StratDB
• Falhas ativas globais do GEM
• Propriedades Minerais RRuff
• artigo -> Sistema evolutivo de mineralogia
• OneGeologia
• catálogo

Irã

Geologia

• Base rochosa OGC BGS 1M

Em geral

• OSF -> Fundação Ciência Aberta
• Metais básicos hospedados em sedimentos -> Metais básicos hospedados em sedimentos
• Limite da Atenosfera da Litosfera -> LAB Hoggard/Czarnota
• Lista de Pesquisa Geológica

Relatórios

Austrália

• GEMIS do Território do Norte
  • procurar
  • exemplo
• SARIG da Austrália do Sul
• Austrália Ocidental WAMEX
  • procurar
  • API
    • exemplo
• Queensland
• Escavações em NSW
  • NSW Escavações abertas
  • API não pública
• PorterGEO -> Bancos de dados mundiais de depósitos minerais com visões resumidas
• Instituto de Minerais Sustentáveis ​​-> Organização de Queensland de pesquisadores afiliados a universidades que produzem conjuntos de dados e conhecimento

Canadá

• Colúmbia Britânica
  • Pesquisa -> Relatórios de Avaliação Mineral
  • Publicações -> Publicações
• Ontário -> Relatórios de Avaliação Mineral
  • Publicações de Ontário
• Alberta
  • Publicações
• Yukon
• Pegada
• Manitoba
• Publicações
• Terra Nova e Labrador
• Territórios do Noroeste
• Nova Escócia
• Quebeque
• Saskatchewan
  • Procurar
  • iMaQs -> Sistema Integrado de Mineração e Pedreiras

EUA

• Arizona
• Montana
• Nevada
• Novo México
• Minesota
• Michigan
• json
• Alasca
• Washington

Outro

• Serviço Geológico Britânico NERC
  • Potencial Mineral
  • Procurar
  • Exemplo de API
  • Publicações
  • MEIGA -> Relatórios do projeto de exploração mineral MEIGA 600 BGS
• GeoLagret -> Suécia
• MinData -> Compilação de localizações rochosas de todo o mundo
• Mineral Databse -> Lista exportável de minerais com propriedades científicas e idades
• NASA
• ResearchGate -> Pesquisador e rede profissional

Ferramentas

SIG

• QGIS -> Visualização e análise de dados GIS Aplicativo de desktop de código aberto, possui algumas ferramentas de ML: Indispensáveis ​​para uma visualização rápida e fácil
  • 2d Geologia em QGIS -> Workshop para QGIS NA 2020 Apresentando mapas geológicos e seções transversais para estudantes e entusiastas
  • OpenLog -> plug -in de broca beta
  • Geo -Sam -> Plugin QGIS para segmento qualquer coisa com rasters
  • Pesos de evidência
  • plug-in
• GRAMA
• Saga -> espelho do fonte do

3D

• Pyvista -> API de embrulho VTK para obter ótima visualização e análise de dados

  • PvGeo
  • Pyvista -Xarray -> Transformando dados XARRAY em VTK 3D INFORMENTE: Uma ótima biblioteca!
  • Omfvista -> pyvista para formato de mineração aberta
  • Scipy 2022 Tutorial

• Pymeshlab -> transformação de malha

• Formato de mineração aberto

• Ferramentas Whitebox

  • GUI -> versão da área de trabalho

• Subsolo

• Geolambda -> AWS Lambda Setup

• Analista de Geociência

  • geoh5py -> Obtendo dados de e para projetos GeoH5
  • Geoapps -> Aplicativos baseados em notebooks para geofísica via geoh5py
  • Geoh5vista
  • GAMS -> Análise de dados magnéticos
    • Artigo - uma estrutura para dados de geociência mineral e portabilidade do modelo - GeoH5

• Rayshader

• Vdeo

General geoespacial

• Recursos Python para ciência da terra
• GEOUTILS -> Análise geoespacial e promover a interoperabilidade entre outros pacotes GIS Python.

Dados vetoriais

Pitão

• Geopandas
  • Dask-geopandas
    • Tutorial
  • Geofileops -> O aumento da velocidade espacial junções via funções de banco de dados e geopackage
• Kart -> Controle de versão distribuída para Daata
• Pyesridump -> Biblioteca para obter dados em escala de servidores de descanso ESRI

R

• SF
• Terra -> Terra fornece métodos para manipular dados geográficos (espaciais) na forma "raster" e "vetor".

Dados de raster

C

• ExactExtract -> Stats Zonal da linha de comando em C

Júlia

• Rastas.jl -> Lendo e escrevendo tipos de dados de varredura comuns

Pitão

• Rasterio -> Biblioteca base Python para tratamento de dados raster
• GeReader -> Processar dados rasteros de diferentes missões de satélite
• Rasterstats -> Resumindo conjuntos de dados de varredura geoespacial com base em geometrias vetoriais
• XARRAY -> Manuseio e análise de matrizes marcadas multidimensionais
  • RioxArray -> API de alto nível fabulosa para manuseio de dados de rasters Xarray
  • Geocube -> Rasterização da API de dados vetoriais
  • ODC -GEO -> Ferramentas para manuseio de varredura baseado em sensoriamento remoto, com muitas ferramentas extremamente úteis, como coloração, fluxos de trabalho da grade
  • Validador de Cog -> Verificando o formato de geotiffs otimizados em nuvem
  • Datacube sem servidor-> Xarray via litops / enrolado / modal
  • Xarray espacial -> Análise estatística de dados de varredura, como classificação como quebras naturais
  • xdggs -> Outros tipos de grades
  • XGCM -> Histogramas com rótulos
  • XRFT -> Transformações de Fourier baseadas em Xarray
  • XVEC -> Cubos de dados vetoriais para Xarray
  • Xarray -einstats -> estatísticas, álgebra linear e einops para Xarray

R

• Raster -> r biblioteca
• Terra -> fornece métodos para manipular dados geográficos (espaciais) na forma "raster" e "vetor".
• Estrelas -> Matrizes espaço -temporais: Raster e Vector Datacubes
• ExactExtracr -> Estatísticas Zonais Raster para R

Referências

• Raster -benchmark -> Benchmarking alguns liberes rasters em Python e R

gui

• Ferramentas WhiteBox -> API Python, GUI, etc. Usaram para o cálculo do índice de umidade topográfica

Coleta de dados

• Piautostage-> 'Uma ferramenta impressa em 3D de código aberto para a coleção automática de imagens de microscópio de alta resolução;' Projetado para amostras minerais.

Conversão de dados

• AEM para seg-y
• ASEG GDF2
• CGG Outfile Reader
• Grid Geosoft para raster
• Loop Geosoft Grid
• Grade de gaita geoso -> Pull Solicy em andamento na conversão para Xarray
• AusCope -> Dados dos modelos binários do Gocad
• Gocad SG Grid Reader
  • Geomodel-2-3dweb-> Aqui eles têm um método para extrair dados das grades binárias do Gocad SG
• Leapfrog Mesh Reader
• OMF -> Formato de mineração aberto para conversão entre as coisas
• Mineiro em pdf
• Vtk para dxf

Geoquímica

• PygeoChemTools -> Linha de biblioteca e comando para ativar o QC rápido e a plotagem de dados geoquímicos
• SA Mapas geoquímicos -> Análise de dados e plotagem de dados de geoquímica da Austrália do Sul da Pesquisa Geológica da SA
• Levenning geoquímico
• Tutorial de geoquímica de Scott Halley
• Tabela periódica

Geoestatística

• Geostatspy
• Pyinterpolate

Geocronologia

• Escala de tempo geológico -> código para produzir, mas também tem um bom CSV regular das idades

Geologia

• Gempy -> Modelagem implícita

• GEMGIS -> Assistência geoespacial de análise de dados

• Loopstructural -> Modelagem de Importabilidade

• Geologia manual de Python -> Análise de dados geológicos

• MAP2LOOP -> Automação de modelagem 3D

  • Loop3d -> gui para map2loop

• Pybedforms

• SA Stratigrafia -> Editor de banco de dados de estratigrafia webApp

• StripLog

• Analise_de_dados_estruturais_altamira

• Tectônica global -> conjunto de dados de código aberto para construir, pratos, margens etc.

  • papel

• adições de zenodo

• Litholog

• Pygplates

• Dados do tutorial

  • papel

Geofísica

• Geocience Australia Utilities
• Geofísica para praticar geocientistas
• Caixa de ferramentas em potencial -> Alguns filtros de transformação de Fastier Fast Fourier baseados em Xarray - derivados, pseudogravidade, RPG etc.
  • Notebook -> Classe com alguns exemplos [derivado vertical, pseudogravidade, continuação ascendente etc.
• Sandbox da geofísica de computação
• Sedimento do porão do ris -> profundidade do porão magnético na Antártica
• Processamento da imagem do sinal

Eletromagnético

• Geociência Austrália AEM
• UH Electromagnetics -> Cursos Cadernos sobre a compreensão deste domínio
• Interpretação AEM
• Emag py -> fdem
• Resipy -> DC / IP

Gravidade e magnetics

• Harmônica
• Exemplos de filtro -> Processamento rápido baseado em transformada de Fourier via Xarray
• Dados da gravidade australiana
• Vermes
• Atualização de worms <- Criação de worm em potencial dos campos com algumas atualizações menores para lidar
• Osborne Magnetic -> Exemplo de processamento de dados da pesquisa

Sísmico

• Segyio
• Segysak -> Manuseio e análise de dados seg -y baseados em Xarray
• Notas geofísicas -> Processamento de dados sísmicos

Magnetotellurics

• Mtpy
• Tutoriais
• Mtpy -> atualização do exposto para facilitar as coisas
• Kit de ferramentas de estatísticas minerais -> Distância para MT Recursos Análise
• Artigo litosférico dos condutores
• Mtwaffle -> Exemplos de análise de dados MT
• pymt
• resistência
• Mecmus -> Ferramentas para ler o modelo de condutividade elétrica dos EUA
• modelo

Grade

• GMT
  • Pygmt
• verde
• GRID_AEROMAG -> Exemplo de grade brasileiro
• pyinterp -> grade multidimensional por impulso
• Pseudogravidade -> De Blakely, 95

Inversão

• Simpeg
  • Mira Geoscience Fork -> Usado para Geoapps
  • Simpeg Fork
  • Transforme 2020 Simpeg
  • Transforme 2021 Simpeg
  • Scripts Simpeg
  • Exemplo de inversão da articulação astic
• Gimli
• Tomofast-x
• USGS Anonymous FTP
• Software USGS -> Lista mais longa de coisas úteis mais antigas: Dosbox, alguém?
• Sub -rotinas geofísicas -> Código fortran
• 2020 Aachen Inversão Problemas -> Visão geral da teoria da inversão da gravidade

Geoquímica

• Pirólito
  • GS2021 Pirólito
• Nivelamento
• Ferramentas PygeoChem
• Geoquimica
• Geochemistrypi
  • papel

Perfuração

• DH2LOOP -> Assistência ao intervalo de perfuração
  • papel
• Drilldown -> Visualização de perfuração em notebooks via Geoh5py -> Nota Desurveying
• Pygslib -> levantamento de fundo de poço e normalização do intervalo
• pyborehole -> processamento e visualização de dados do poço
• DHCOMP -> Dados geofísicos de compósitos para um conjunto de intervalos

Sensoriamento Remoto

• Indices espectrais impressionantes -> Guia para criação de índice espectral
• Cubo de dados aberto
  • Notebooks DEA -> Código para uso em fluxos de trabalho no estilo ODC
  • Datacube -Stats -> Biblioteca de Análise Estatística para ODC
  • Notebooks Geo -> Exemplos de código do elemento 84
• Raster4ml -> um grande número de índices de vegetação
• Lefa -> Análise de fratura, lineamentos

Sem servidor

• Kerchunk -> Acesso sem servidor a dados baseados em nuvem via Zarr
  • Kerchunk Geoh5 -> Acesso ao Analista Geociente/GeoH5 Projetos sem servidor via Kerchunk
  • ICEHUNK -> Motor de armazenamento transacional para dados de tensor / nd -matray projetados para uso no armazenamento de objetos em nuvem.

Catálogos STAC

• DEA STACKSTAC -> Exemplos de trabalho com dados digitais da Earth Australia
• INDAKE-STAC
• ML AOI Extensão
• Especificação de extensão do modelo ML -> Especificação do modelo de aprendizado de máquina para catalogingspatio -temporal Models
  • papel
• ODC -STAC -> Cubo de dados aberto gratuito do banco de dados
• Pystac
• Pesquisa SAT
• StackStac -> Metadados aceleraram o Dask e Xarray TimeRies

Estatísticas

• Orange -> GUI de mineração de dados
• Hdstats -> Base algorítmica de medianas geométricas
• Hdmedians

Visualização

• TV -> Visualizar imagens de satélite em um terminal
• Titiler
• Senta
• HSDAR
• Estrelas
• Sar de mineração de ouro do Peru

Potencial mineral

• Mapeamento potencial mineral de níquel -> Análise baseada em ESRI
• Ferramenta online de prospecção

Economia de mineração

• BlueCap -> Framework da Monash University para avaliar a viabilidade de minas
• Lei ZIPFS ​​-> Curva Ajustando a distribuição de depoimentos minerais
• Pyasx -> ASX Data Feed Retraping
• API de preço de metal -> Microservice de contêineres

Visualização

• Napari -> Visualizador de imagem multidimensional
• HoloViews -> Visualização de dados em larga escala
• GraphViz -> Plotagem de gráfico/assistência de visualização Informações de instalação do Windows
• KDE espacial

Colormaps

• Coloras de uniformes perceptivamente CET
• PU COLORMAPS -> formatado para usuário em analista de geociência
• Distorções em Colormap -> Um aplicativo de painel para demonstrar distorções criadas por coloras não perceptivas em dados geofísicos
• Ripping Data of Colormpas
• Projetos de código de geociência aberta

Geoespacial

• Geoespacial>- Instala vários pacotes comuns de python
• Lista Geoespacial Python -> com curadoria

Pilhas de tecnologia

C

• GDAL -> Quadro de transformação e análise de dados absolutamente cruciais
  • Ferramentas -> Nota tem muitas ferramentas de linha de comando também muito úteis

Júlia

• Julia Earth -> Promovendo ciência geoespacial de dados e modelagem geoestatística em ciências da terra
• Julia Geodynamics -> Código de Geodinâmica Computacional
• Introdução a Julia for Geoscience

Python - Pydata

• ANACONDA -> Receba lotes já instalados com este gerenciador de pacotes.
  • Gdal et al -> Tire a dor do GDAL e do TensorFlow instala aqui
  • Git Bash -> Fazer com que o CONDA funcione em Git Bash
• Matrizes multidimensionais numpy
• Análise de dados tabulares de pandas
• Visualização de matplotlib
• Zarr -> Arrays distribuídos compactados e emitidos
• Dask -> Computação paralela e distribuída
  • Dask Cloud Provider -> Inicie automaticamente os clusters de Dask na nuvem
  • Dask mediana -> Notebook que fornece um protótipo de função mediana de DASK
• Ecossistema geoespacial de Python -> Informações com curadoria

Ferrugem - Georust

• Georust -> Coleção de utilitários geoespaciais em ferrugem

Bancos de dados

• DuckDB -> em processo OLAP DB em velocidade - possui alguns recursos geoespaciais e de matriz
• IBIS + DuckDB Geopopsatial -> Scipy2024 Talk

Ciência de Dados

• Modelo de ciência de dados Python -> Configuração do pacote de projetos
• Awesome Python Data Science -> Guia com curadoria

Probabilidade

• Distfit -> ajuste de densidade de probabilidade

Ciência

• Recursos Python para Ciências da Terra
• Computação científica impressionante

Docker

• AWS Deep Learning Recxiers
• Docker espacial
• DL Docker Geoespacial
• Balancim
• Docker Lambda
• Geobase
• DL Docker Geoespacial

Ontologias

• Vocabulários da Sociedade Geológica de Queensland
  • Banco de dados de propriedades geológicas
  • Geofeatures
• Sociedade Geológica da Austrália Ocidental
• Estratigráfico
• Gerente de Conhecimento da Geociência
• Vocabulários geosciml

Livros

Pitão

• Livro de receitas de análise geoespacial do Python
• Geoprocessamento com Python -> Manning Livebook

Outro

• Livro didático
• Aprendizado de máquina na indústria de petróleo e gás
• Geocomputação com r
• Livro de receitas de EarthData Cloud -> Como acessar os recursos da NASA
• Livro de receitas do Limpador de dados -> Colocando as ferramentas do Unix em bom uso para disputa e limpeza de dados
• Enciclopédia de geociências matemáticas
• Manual de Geociências Matemáticas

Outro

• GXPY -> API da Geosoft Python
• Eartharxiv -> Download de trabalhos do arquivo de pré -impressão
• Esoar -> Arquivo de papel de pré -impressão

Conjuntos de dados

Mundo

Geologia

• Bedrock -> Geologia generalizada do mundo
• Glim -> mapa de litologia global
• Paleogeologia Um atlas de mapas paleogeográficos fanerozóicos
• Camadas sedimentares -> espessura global de 1 km de grade de solo, regolito e depósito sedimentar
• Mapa de estresse mundial -> Compilação global de informações sobre o campo de estresse atual da crustal
• GMBA -> Inventário Global de Montanha

Geofísica

Gravidade

• Curvatura -> Análise global de curvatura a partir de dados de gradiente de gravidade
• WGM 2012

Magnetics

• Eamg2v3 _> grade de anomalia magnética da terra
• Wdmam -> mapa de anomalia magnética digital mundial

Magnetotellurics

• EMC -> Modelo inverso 3D global de condutividade elétrica

Sísmico

• Laboratório slnaafsa
• Laboratório CAM2016
• MOHO -> dados Gemma
• MOHO -> DATOS SZWILLUS
• Velocidade sísmica -> Debayle et al
• Lithoref18 -> Um modelo de referência global da litosfera e manto superior da inversão e análise de vários conjuntos de dados
• Crust1.0 -> Modelo Crustal Global NETCDF
• Visão geral Página inicial

Térmico

• Fluxo de calor

Em geral

• TEMPO DIGITAL DE PROFUNDO -> Dados e visualização para uma variedade de fontes de dados e modelos
• Earthchem -> Preservação, Descoberta, Acesso e Visualização orientados pela comunidade de dados geoquímicos, geocronológicos e petrológicos
• Georoc -> Composição geoquímica de rochas
• Geologia Global -> Uma receita curta para fazer um mapa de geologia global no formato GIS (por exemplo, shapefile), com faixas etárias mapeadas para o GTS2020 TimeScale
• Grandes Comissão das Províncias Igênicas
• Plumas do manto
• Espessura de sedimentos -> mapa
• SpatialReference.org -> Repositório para o site

Austrália

• Modelo de Terra Comum
• Mapa mineral pesado
• Mapa mineral pesado do piloto da Austrália
• Aplicativo brilhante

Geoquímica

• Grades preditivas das principais concentrações de óxido em rochas e regolith superficiais sobre o continente australiano -> vários óxidos

Geologia

• Atlas de rochas alcalinas
• Cenozóico
• Mesozóico
• Paleozóico
• Archaean
• procurar
• Rochas alcalinas proterozóicas -> alcalina proterozóica e rochas ígneas relacionadas da Austrália GIS
• Cenozóico
• Mesozóico
• Paleozóico
• Archaean
  • Paper https://ecat.ga.gov.au/geonetwork/srv/eng/catalog.search#/metadata/147963
• Hidrogeologia -> Mapa de hidrogeologia da Austrália
• Hidrogeologia -> 5m
• Geologia em camadas -> 1M
• Geologia da superfície -> 1M de escala
• O conjunto de dados GIS Australian MAFF-ULTRAMAFIC MAGMATIC EVENTS GIS

Geofísica

• Gravity -> 2019 Grades nacionais de gravidade australiana

Magnetics

• TMI -> mapa de anomalia magnética da Austrália, sétima edição, 2019 TMI
• 40m -> 40m versão
• VRTP -> Grade total de intensidade magnética (TMI) da Austrália com redução variável para o polo (VRTP) 2019
• 1VD -> Grid de intensidade magnética total da Austrália 2019 - Primeira derivada vertical (1VD)

Radiometrics

• Radiometrics -> Grade radiométrica completa da Austrália (RADMAP) V4 2019 com preenchimento modelado
• K -> Grade radiométrica da Austrália (RADMAP) V4 2019 Filtrado PCT Potassium Grid
• U -> Grade radiométrica da Austrália (RADMAP) V4 2019 PPM filtrado PPM URNIONIO
• TH -> Grid radiométrica da Austrália (RADMAP) V4 2019 Filtrado PPM Thorium
• TH/K -> Grid radiométrica da Austrália (RADMAP) V4 RAÇÃO 2019 TORIUO SOBRE O POTÁSSIO
• U/K -> Grade radiométrica da Austrália (RADMAP) V4 RAÇÃO 2019 URNIONIME SOBRE O POTÁSSIO
• U/th -> grade radiométrica da proporção da Austrália (RADMAP) V4 2019
• U Squared/Th -> Grid Radiométrica da Austrália (RADMAP) V4 RATIO 2019 URNIOM SQUED SQUED SOBRE TORIUM
• Taxa de dose-> Grid radiométrica da Austrália (RADMAP) V4 2019 Taxa de dose terrestre filtrada
• Imagem ternária -> Grid radiométrica da Austrália (RADMAP) V4 2019 - Imagem ternária (K, Th, U)

AUSAEM

• AUSAEM 1 -> AUSAEM Ano 1 NT/QLD Pesquisa eletromagnética no ar; Produtos de inversão da terra em camadas GA
• AUSAEM 1 -> AUSAEM Ano 1 nt/qld: TEMPEST® Airborne Electromagnetic Data e EM Flow® Condutivity Estimativas
• Pacote de dados de interpretação AUSAEM 1 -> AUSAEM1
• AUSAEM 2 -> AUSAEM 02 WA/NT 2019-20 Pesquisa eletromagnética no ar
• AUSAEM -WA -> AUSAEM -WA, Murchison Airborne Electromagnetic Blocks
• AUSAEM-WA-> AUSAEM-WA, SULBO-ALBANY AIRBORNEIRA BLOCOS DE PESQUISA ENCROMAGNÉTICA
• AUSAEM -WA -> AUSAEM WA 2020-21, Eastern Goldfields e East Yilgarn Airbatborne
• AUSAEM -WA -> AUSAEM (WA) 2020-21, EarAheedy & Desert Strip
• AUSAEM ERC -> AUSAEM ORIENTERN RECORPESS CORRUDOR
• AUSAEM WRC -> AUSAEM WESTERN RECURSOS CORRUDOR
• Visão geral interp
• Grades nacionais de condutividade de superfície e quase superfície -> Interpolação nacional de ML para Ausem de maneira semelhante ao norte da Austrália

AUSLAMP

• Sea de Auspo Ausso -> Modelo de Resistividade do Sudeste Australiano continente a partir de dados magnetotelúticos de auspo
• Dados de Victoria
• Dados de NSW
• AusLamp TISA -> Modelo de Resistividade derivado de Magnetotelurics: AusLamp -Tisa Project
• Modelo de resistividade litosférica de AusLamp Delameria
• AusLamp ne sa
• AusLamp Gawler
• Estações AusLamp -> por volta de 2017
• Tasmanides Paper

Moho

• Moho 2019 e 2023
  • papel

Depósitos minerais

• Cenário geológico, idade e doação dos principais depósitos minerais australianos
• Um conjunto de dados abrangente para a produção australiana de minas 1799 a 2021
  • papel

Potencial mineral

• Visão geral - Geoscience Australia -> Visão geral das publicações e conjuntos de dados
• Sedimentos hospedados em zinco
• Relatório
• Sedimentos hospedados em cobre
• Relatório
• Resumo
• Elementos de terras raras carbonatitas
  • papel

Desperdício de minas

• Desperdício de minas australiano

Título nativo

• Tribunal de títulos nativos nacionais

Sensoriamento Remoto

• Landsat Bare Earth - Bare Earth mediana do Landsat
• Imagens mais aprimoradas do Landsat Landsat para modelagem solo e litológica: conjunto de dados -> Detalhes de um aprimoramento
• A pegada de mineração global mapeada de imagens de satélite de alta resolução ** papel
• Dem -> Austrália 1 Srtm Dem de várias variedades

Estrutura

• Principais limites da crustal da Austrália - edição de 2024

Velocidade

• Au Tomo -> modelo de velocidade de próxima geração da crosta australiana de imagens de ruído ambiente síncrono e assíncrono

Topografia

• Posição topográfica em várias escalas - RGB
• Informações
• Índice de umidade topográfica - 1 e 3 segundos
• Informações
• Índice de posição topográfica - 1 e 3 segundos
• Informações
• Modelo de intensidade de intemperismo
  • papel
• Informações
• {Info] (https://researchdata.edu.au/weathering-intension-model-australia/1361069)

Norte

• Espessura da tampa Tisa -> Pontos de espessura da tampa para Tennant Creek Mt Isa com grades interpoladas
• Mapeamento de condutividade de alta resolução usando a pesquisa regional da AEM e o aprendizado de máquina -> ml de interpolação de condutividade para ausaem
• Resumo estendido
• Geologia Sólida -> Geologia Sólida do Craton do Norte da Austrália
• Modelos de inversão -> Os modelos de gravidade 3D da Austrália Craton e inversão magnética
• Ni-CU-PGE-> Potencial para depósitos de sulfeto de Ni-Cu-PGE hospedados por intrusões na Austrália: uma análise em escala continental da prospectividade do sistema mineral
• TISA IOCG -> Avaliação potencial mineral de óxido de óxido de ferro (IOCG) para Tennant Creek -MT ISA Região: Dados geoespaciais
• Alteração TISA -> Produzindo proxies de alteração de magnetita e hematita usando gravidade 3D e inversão magnética

Austrália do Sul

Geologia

• Geologia da base
• Porão cristalino -> bruscos cristalinos que se cruzam
• Minas e depósitos minerais
• Furos minerais
• Geologia sólida 3d
• 100k falhas
• Archaean
• Falhas arqueanas
• Mesoproterozóico -> meio
• Mesoproterozóico -> falhas médias
• Mesoproterozóico -> tarde
• Falhas mesoproterozóicas -> falhas tardias
• Neoproterozóico
• Falhas neoproterozóicas
• Modelo 3D de cobre sedimentares de prateleira de prateleira
• Geologia da superfície

Geofísica

• AusLamp 3D -> inversões magnetotelúticas
• GCAS -> Pesquisa Airborne de Gawler Craton
• Gravidade -> grades de gravidade
• Estações -> Estações de gravidade
• Magnetics -> Magnetics
• Linhas sísmicas -> linhas sísmicas

Gawler

• Gawler MPP -> Projeto de promoção mineral de Gawler - dados

Queensland

• Visão geral
• Deep Mining Queensland-> Deep Mining Queensland
• Atlas de depósito -> Atlas da província mineral do noroeste Atlas
• Geologia -> Visão geral da série de geologia
• Relatório de Mineral e Energia -> Relatório do Noroeste de Minerais e Energia de Queensland 2011 -NWQMEP
• Vectoring -> Vetoring de geoquímica mineral
• Poços de petróleo
• Poços de gás de costura de carvão
• Furos

CLONCURRY

• Kit de ferramentas -> Kit de ferramentas e laboratório multielement

Território do Norte

• ARUNTA IOCG-> Potencial de óxido de Óxido de ferro da região do sul de Arunta
• Urânio do Sul -> Território do Norte do Sul de Urânio e Sistemas de Energia Geotérmica Pacote de dados Digil Digil
• Tennant Creek -> Modelo de condutividade derivado de dados magnetotelúticos na região de Tennant East, Território do Norte

Nova Gales do Sul

Geologia

• Geologia sem costura -> Pacote de dados de geologia sem costura NSW (versão mais antiga também nesta página)

Pacotes de dados em potencial minerais

• Curnamona
• Lachlan Oriental
• LACHLAN CENTRAL
• Southern New England

Austrália Ocidental

Geoquímica

• MAFICO/ULTRAMAFIC

Geologia

• Bedrock de 100k
• Mapheets de 100k para a superfície Você precisa baixar individualmente e combinar - eles não são consistentes
• Mapheets de 250k para a superfície Você precisa baixar individualmente e combinar - eles não são consistentes
• Bedrock de 500k
• Minas abandonadas
• Ocorrências minerais

Potencial mineral

• Níquel de Komatiite
• Relatório

Prospecção

• CAPRICORN-> Análise de prospecção usando uma abordagem de sistemas minerais - Projeto de Estudo de Caso Capricórnio
• King Leopold -> Prospecção mineral da prateleira do rei Leopold Orogen e Lennard: Análise de possíveis dados de campo na região de West Kimberley
• Yilgarn Gold
• Yilgarn 2 -> Descoberta mineral preditiva no leste de Yilgarn Craton: Um exemplo de direcionamento em escala de distrito de um sistema mineral de ouro orogênico
• [Nota da loja] -> WA possui alguns pacotes de prospecção disponíveis para compra no USB Drive por preços do tipo 50-60AU -veja na seção de mapas geopasitais

Tasmânia

Geologia

• 250 mil
• 500 mil
• 25 mil
• Ocorrências minerais
• Modelo 3D

Vitória

• Geologia sem costura

Nova Zelândia

• Pacote de dados minerais -> pacote de dados de exploração mineral

Norte da America

• Dados e grades de recursos geofísicos, geológicos e minerais em escala nacional -> também possui alguns dados da Austrália
• Poços de água subterrânea -> banco de dados
• Dados máximos horizontais de estresse e magnitude do estresse relativo (regime de falhas) em toda a América do Norte

Canadá

Geologia

• Mapa
• Geologia -> Mapa de geologia de rochas atualizado
• Geologia -> Compilação de geologia da rocha e síntese regional do sul de Rae e partes de Hearne, Província de Churchill, Territórios do Noroeste, Saskatchewan, Nunavut, Manitoba e Alberta
• MOHO -> Banco de dados nacional de estimativas de profundidade moho estimativas de refração sísmica e pesquisas teleseísmicas

Geofísica

• Pesquisa do DAP -> Pesquisa geoportal - Observe irritantemente estas em Geosoft Grids - Veja o outro
• [Gravidade, magnetics, radiometrics] -> Principalmente escala do país

Europa

Finlândia

• Fodd -> depósitos minerais da Fennoscandiana

Irlanda

• MPM -> Projeto de mapeamento de potencial mineral

Documentos com código

PNL

• https://www.sciencedirect.com/science/article/pii/s2590197422000064?via%3DiHub#bib20- -> Modelos de linguagem geocience e sua avaliação intrínseca -> Código NRCO acima [Inclui modelo]
• https://www.researchgate.net/publication/334507958_word_embeddings_for_application_in_geosciences_development_evaluation_and_examples_of_soil -Related_concepts -> Geovec [Inclui Model]
• https://www.researchgate.net/publication/347902344_portuguese_word_embeddings_for_the_oil_and_gas_industry_development_and_evaluation -> Petrovec [Inclui modelo]
• Um recurso para pesquisa e agrupamento automatizados de conjuntos de dados geoquímicos de suplementos de periódicos

Geoquímica

• https://www.researchgate.net/publication/365758387_a_resource_for_automated_search_and_collation_of_geochemical_datasets_from_journal_suplements
  • https://github.com/erinlmartin/figshare_geoscrape?s=09

Geologia

• https://github.com/sydney-machine-learning/autoncoders_remotesensing-> autoencoders empilhados para mapeamento litológico

Mineral

• https://www.researchgate.net/publication/318839364_network_analysis_of_mineralogical_systems

Documentos com dados de recursos

• Você pode reproduzir a saída geoespacialmente dos dados fornecidos.

Prospectividade mineral

• https://www.sciencedirect.com/science/article/pii/s016913682100010x#s0135 -> Modelagem de prospecção de sistemas minerais magmáticos canadenses (± cu ± co ± pge) sistemas minerais de sulfide [leitura do valor do bem]
• https://www.sciencedirect.com/science/article/pii/s01691368210066612#b0510 -> Modelagem de prospecção dada a dados de sistemas minerais Zn -PB de sedimentos e suas matérias -primas críticas [Leitura de valor bem]
• https://www.researchgate.net/publication/358956673_towards_a_ly_data-driven_prospectivity_mapping_methodology_a_case_study_of_the_southeastern_churchill_province_and_land_landy_of_the_southeastern1

Inglaterra

• https://www.researchgate.net/publication/358083076_machine_learning_for_geochemical_exploration_classify_metalogenic_fertility_deppos_arc_magmas_insights_into

Geoquímica

• https://www.researchgate.net/publication/361076789_automated_machine_learning_pipeline_fore:geoChemical_analysis

Geologia

• https://eprints.utas.edu.au/32368/ -> Modelagem Assistida por Máquina de Litologia e Metasomatismo

Geofísica

• https://github.com/tomasnaprstek/aeromagnetic_cnn - CNN aeromagnética
• Paper https://www.researchgate.net/publication/354772176_convolution_neural_networks_applied_to_the_interpretation_of_lineaments_in_aeromagnetic_data
• PhD -> Novos métodos para a interpolação e interpretação de lineamentos em dados aeromagnéticos
• Paper https://www.researchgate.net/publication/354772176_Convolution_Neural_Networks_Applied_to_the_Interpretation_of_Lineaments_in_Aeromagnetic_Data -> Convolution Neural Networks Applied to the Interpretation of Lineaments in Aeromagnetic Data

Saída geoespacial - sem código

• https://geoscience.data.qld.gov.au/report/cr113697 -> NWMP Exploração mineral dada a dados e mapeamento geológico [CSIRO também]

Diários

• https://www.sciencedirect.com/journal/artificial-intelligence-in-geosciences-> Inteligência artificial em geociências

Artigos

• Geralmente não ML, ou nenhum código/dados e às vezes nenhuma disponibilidade
• Eventualmente, se separará em coisas que possuem pacotes de dados ou não gostam de estudos da Zona NSW.
• No entanto, se estiver interessado em uma área, muitas vezes você poderá obter uma imagem, se nada mais como um guia difícil.
• Geralmente estes não são reproduzíveis - alguns como os estudos da zona de prospecção de NSW e o NWQMP estão com algum trabalho.
• O artigo ocasional nesta seção pode ser listado acima

Novo para arquivar

Em geral

• https://www.researchgate.net/publication/337650865_a_combinative_knowledge-driven_integration_method_for_integrating_geophysical_layers_with_geological_and_geochemical_datasets
• https://link.springer.com/article/10.1007/s11053-023-10237-w-Uma nova geração de algoritmos de inteligência artificial para mapeamento de prospecção mineral
• https://www.researchgate.net/publication/235443297_addressing_challenges_with_exploration_dataSets_to_geneate_usable_mineral_potencial_maps
• https://www.researchgate.net/publication/330077321_An_Improved_Data-Driven_Multiple_Criteria_Decision-Making_Procedure_for_Spatial_Modeling_of_Mineral_Prospectivity_Adaption_of_Prediction-Area_Plot_and_Logistic_Functions
• Inteligência artificial para exploração mineral: uma revisão e perspectivas sobre direções futuras da Ciência de Dados -> https://www.sciencedirect.com/science/article/pii/s0012825224002691
• https://www.researchgate.net/project/bayesian-machine-learning-for-geological-modeling-and-geophysical-segmentation
• https://www.researchgate.net/publication/229714681_classifiers_for_modeling_of_mineral_potencial
• https://www.researchgate.net/publication/352251078_data_analysis_methods_for_prospectivity_modelling_as_applied_to_mineral_exploration_targeting_state-of-tart_and_out_outload
• https://www.researchgate.net/publication/267927728_data-driven_evildial_belief_modeling_of_mineral_potencial_using_few_prospects_and_evidence_with_missing_values
• https://www.linkedin.com/pulse/deep-learning-meets-wardward-continuation-caldera-analytics/?trackingid=ybkv3ukni7ygh3irchzdgw%3d%3d
• https://www.researchgate.net/publication/382560010_dinov2_rocks_geological_image_analysis_classification_segmentation_and_interpretabilidade
• https://www.researchgate.net/publication/368489689_discrimination_of_pb-zn_deposit_types_using_sphalerite_geochemistry_new_insights_from_machine_learning_algorithm
• https://link.springer.com/article/10.1007/s11430-024-1309-9-> Modelos de inteligência artificial explicáveis ​​para mapeamento de prospecção mineral
• https://www.researchgate.net/publication/229792860_from_predictive_mapping_of_mineral_prospectivity_to_quantitative_estimation_of_number_of_undiscovered_prospects
• https://www.researchgate.net/publication/339997675_ly_reversible_neural_networks_for_large-scale_surface_and_sub-surface_characterization_via_remote_sensing
  • arxiv
  • apresentação
  • conferência
  • Juliacon
• https://www.researchgate.net/publication/220164488_geocomputação_of_mineral_exploration_targets
• https://www.researchgate.net/publication/272494576_geological_knowledge_discovery_and_minerais_targeting_from_regolith_using_a_machine_learning_approach
• https://www.researchgate.net/publication/280013864_geometric_averrage_of_spatial_evidence_data_layers_a_gis baseou_multi-critria_decision-making_approach_to_mineral_prospativity
• https://www.researchgate.net/publication/355467413_harnessing_the_power_of_artificial_intelligence_and_machine_learning_in_mineral_exploration-opportunities_and_cauthery_notes
• https://www.researchgate.net/publication/335819474_importance_of_spatial_predictor_variable_selection_in_machine_learning_application_---Moving_from_data_reproduction_to_spatial_pradiction
• https://www.researchgate.net/publication/337003268_improted_supervised_classification_of_bedrock_in_areas_of_transported_overburden_apply_domain_expertise_at_kerkasha_eritrea -gazley
• https://www.researchgate.net/publication/360660467_litospheric_conductors_reveal_source_regions_of_convergent_margin_mineral_systems
• https://api.research-repository.uwa.edu.au/portalfiles/portal/5263287/lysytsyn_volodymyr_2015.pdf (tese de doutorado) baseado em GIS para explicar a prospectividade de cobre da MT Isa, australia: austrália: Implicações para a Austrália para a Austrália: Implicações para a Austrália: Implicações para a Austrália: Implicações para a Austrália: a Austrália: a Austrália: Implicações para a Austrália: a Austrália: a Austrália: a Austrália: a Austrália: a Austrália.
• https://www.researchgate.net/publication/374972769_knowledge_and_technology_transfer_in_and_beyond_mineral_exploration -> Conhecimento e transferência de tecnologia para e além da exploração mineral
• https://www.researchgate.net/publication/331946100_machine_learning_for_data-driven_discovery_in_solid_earth_geoscience
• https://theses.hal.science/tel-04107211/document-abordagens de aprendizado de máquina para fontes heterogêneas geológicas da sub-superfície
• https://www.researchgate.net/publication/309715081_Magmato-hydrothermal_space_A_new_metric_for_geochemical_characterisation_of_metallic_ore_deposits - Magmato-hydrothermal space: A new metric for geochemical characterisation of metallic ore deposits
• https://www.researchgate.net/publication/220164234_mapping_complexity_of_spatial_distribution_of_faults_using_fractal_and_multifractal_models_vectoring_towards_exploration_targets
• https://www.researchgate.net/publication/220163838_objective_selection_of_suitable_unit_cell_size_in_data-driven_modeling_of_mineral_prospectivity
• https://www.researchgate.net/publication/273500012_prediction-area_p-a_plot_and_c-a_fractal_analysis_to_classify_and_evalate_evinel_maps_fore_mertalal_prospectivity_modeling
• https://www.researchgate.net/publication/354925136_soil-sample_geochemistry_normilised_by_class_membership_from_machine-learnt_clusters_of_satellite_and_geophysics_data [Gazley
• https://link.springer.com/article/10.1007/s12665-024-11870-1-> quantificação da incerteza dos mapas geocientíficos que se baseiam no engajamento sensorial humano
• https://www.researchgate.net/publication/235443294_the_effect_of_map-cale_on_geological_complexity
• https://www.researchgate.net/publication/235443305_the_effect_of_map_scale_on_geological_complexity_for_computer-ed_exploration_targeting
• https://link.springer.com/article/10.1007/s11053-024-10322-8-> incerteza induzida por fluxo de trabalho no mapeamento de prospecção mineral orientado a dados

Prospectividade mineral

Austrália

• https://www.mdpi.com/2072-4292/15/16/4074-> Uma abordagem espacial dada a dados para mapeamento de prospecção mineral
• https://www.researchgate.net/publication/353253570_a_truly_spatial_random_forests_algorithm_for_gescience_data_analysis_and_modelling
• https://www.researchgate.net/publication/253217016_advanced_methodologies_for_the_analysis_of_databases_of_mineral_deposits_and_major_faults
• https://www.researchgate.net/publication/362260616_assessing_the_impact_of_conceptual_mineral_systems_uncertty_on_prospectivity_predictions
• https://www.researchgate.net/publication/352310314_central_lachlan_mineral_potencial_study
• https://meg.resourcesGulator.nsw.gov.au/sites/default/files/2024-05/eith%202024%20muller_exploration_in_the_house_keynote.pdf -> minerais críticos -prospectividade usando a generativa AI
• https://www.tandfonline.com/doi/pdf/10.1080/22020586.2019.12073159?needaccess=true -> Integração de uma abordagem de sistemas de minerais com aprendizado de máquina: um estudo de caso da 'exploração de minerais modernos' em MT Woods Inlier - Gawler Craton, Austrália do Sul
• https://www.researchgate.net/publication/365697240_mineral_potencial_modelling_of_orogenic_gold_systems_in_the_granites-tanami_orogen_northern_territory_australia_a_mocrata
• https://publications.csiro.au/publications/publication/picsiro:ep2022-0483 -> Assinaturas dos principais sistemas minerais na província de Eastern Mount Isa, Queensland: novas perspectivas da Analytics de dados
• https://link.springer.com/article/10.1007/s11004-021-09989-z-> Modelagem estocástica de metas de exploração mineral
• https://www.researchgate.net/publication/276171631_supervised_neural_network_targeting_and_classification_analysis_of_airborne_em_magnetic_and_and_sploration_spectrometry_data_minerher_em_magnetic_and_and_splorat
• https://www.researchgate.net/publication/353058758_Using_Machine_Learning_to_Map_Western_Australian_Landscapes_for_Mineral_Exploration
• https://www.researchgate.net/publication/264535019_Weights-of-evidence_and_logistic_regression_modeling_of_magmatic_nickel_sulfide_prospectivity_in_the_Yilgarn_Craton_Western_Australia

Argentina

• https://www.researchgate.net/publication/263542691_ANALYSIS_OF_SPATIAL_DISTRIBUTION_OF_EPITHERMAL_GOLD_DEPOSITS_IN_THE_DESEADO_MASSIF_SANTA_CRUZ_PROVINCE
• https://www.researchgate.net/publication/263542560_EVIDENTIAL_BELIEF_MAPPING_OF_EPITHERMAL_GOLD_POTENTIAL_IN_THE_DESEADO_MASSIF_SANTA_CRUZ_PROVINCE_ARGENTINA
• https://www.researchgate.net/publication/277940917_Porphyry_epithermal_and_orogenic_gold_prospectivity_of_Argentina
• https://www.researchgate.net/publication/269518805_Prospectivity_for_epithermal_gold-silver_deposits_in_the_Deseado_Massif_Argentina
• https://www.researchgate.net/publication/235443303_Prospectivity_mapping_for_multi-stage_epithermal_gold_mineralization_in_Argentina

Brasil

• https://www.researchgate.net/publication/367245252_Geochemical_multifractal_modeling_of_soil_and_stream_sediment_data_applied_to_gold_prospectivity_mapping_of_the_Pitangui_Greenstone_Belt_northwest_of_Quadrilatero_Ferrifero_Brazil
• https://www.researchgate.net/publication/381880769_How_do_non-deposit_sites_influence_the_performance_of_machine_learning-based_gold_prospectivity_mapping_A_study_case_in_the_Pitangui_Greenstone_Belt_Brazil
• https://www.researchsquare.com/article/rs-5066453/v1 -> Enhancing Lithium Exploration in the Borborema Province, Northeast Brazil: Integrating Airborne Geophysics, Low-Density Geochemistry, and Machine Learning Algorithms
• https://www.researchgate.net/publication/362263694_Machine_Learning_Methods_for_Quantifying_Uncertainty_in_Prospectivity_Mapping_of_Magmatic-Hydrothermal_Gold_Deposits_A_Case_Study_from_Juruena_Mineral_Province_Northern_Mato_Grosso_Brazil
• https://www.researchgate.net/publication/360055592_Predicting_mineralization_and_targeting_exploration_criteria_based_on_machine-learning_in_the_Serra_de_Jacobina_quartz-pebble-metaconglomerate_Au-U_deposits_Sao_Francisco_Craton_Brazil

Difuso

• https://www.researchgate.net/publication/272170968_A_Comparative_Analysis_of_Weights_of_Evidence_Evidential_Belief_Functions_and_Fuzzy_Logic_for_Mineral_Potential_Mapping_Using_Incomplete_Data_at_the_Scale_of_Investigation A Comparative Analysis of Weights of Evidence, Evidential Belief Functions, and Fuzzy Logic for Mineral Potential Mapping Using Incomplete Data at the Scale of Investigation
• https://www.researchgate.net/publication/360386350_Application_of_Fuzzy_Gamma_Operator_to_Generate_Mineral_Prospectivity_Mapping_for_Cu-Mo_Porphyry_Deposits_Case_Study_Kighal-Bourmolk_Area_Northwestern_Iran
• https://www.researchgate.net/publication/348823482_Combining_fuzzy_analytic_hierarchy_process_with_concentration-area_fractal_for_mineral_prospectivity_mapping_A_case_study_involving_Qinling_orogenic_belt_in_central_China
• https://tupa.gtk.fi/raportti/arkisto/m60_2003_1.pdf -> Conceptual Fuzzy Logic Prospectivity Analysis of the Kuusamo Area
• https://www.researchgate.net/publication/356508827_Geophysical-spatial_Data_Modeling_using_Fuzzy_Logic_Applied_to_Nova_Aurora_Iron_District_Northern_Minas_Gerais_State_Southeastern_Brazil
• https://www.researchgate.net/publication/356937528_Mineral_prospectivity_mapping_a_potential_technique_for_sustainable_mineral_exploration_and_mining_activities_-_a_case_study_using_the_copper_deposits_of_the_Tagmout_basin_Morocco

Canadá

• http://www.geosciencebc.com/i/pdf/SummaryofActivities2015/SoA2015_Granek.pdf -> Advanced Geoscience Targeting via Focused Machine Learning Applied to the QUEST Project Dataset, British Columbia
• https://open.library.ubc.ca/soa/cIRcle/collections/ubctheses/24/items/1.0340340 -> Application of machine learning algorithms to mineral prospectivity mapping
• https://www.researchgate.net/publication/369599705_A_study_of_faults_in_the_Superior_province_of_Ontario_and_Quebec_using_the_random_forest_machine_learning_algorithm_spatial_relationship_to_gold_mines
• https://www.researchgate.net/publication/273176257_Data-_and_Knowledge_driven_mineral_prospectivity_maps_for_Canada's_North
• https://www.researchgate.net/publication/300153215_Data_mining_for_real_mining_A_robust_algorithm_for_prospectivity_mapping_with_uncertainties
• https://www.sciencedirect.com/science/article/pii/S1674987123002268 -> Development and application of feature engineered geological layers for ranking magmatic, volcanogenic, and orogenic system components in Archean greenstone belts
• https://qspace.library.queensu.ca/bitstream/handle/1974/28138/Cevik_Ilkay_S_202009_MASc.pdf?sequence=3&isAllowed=y -> MACHINE LEARNING ENHANCEMENTS FOR KNOWLEDGE DISCOVERY IN MINERAL EXPLORATION AND IMPROVED MINERAL RESOURCE CLASSIFICATION
• https://www.researchgate.net/publication/343511849_Identification_of_intrusive_lithologies_in_volcanic_terrains_in_British_Columbia_by_machine_learning_using_Random_Forests_the_value_of_using_a_soft_classifier
• https://www.researchgate.net/publication/365782501_Improving_Mineral_Prospectivity_Model_Generalization_An_Example_from_Orogenic_Gold_Mineralization_of_the_Sturgeon_Lake_Transect_Ontario_Canada
• https://www.researchgate.net/publication/348983384_Mineral_prospectivity_mapping_using_a_VNet_convolutional_neural_network
• corporate link
• https://www.researchgate.net/publication/369048379_Mineral_Prospectivity_Mapping_Using_Machine_Learning_Techniques_for_Gold_Exploration_in_the_Larder_Lake_Area_Ontario_Canada
• https://www.researchgate.net/publication/337167506_Orogenic_gold_prospectivity_mapping_using_machine_learning
• https://www.researchgate.net/publication/290509352_Precursors_predicted_by_artificial_neural_networks_for_mass_balance_calculations_Quantifying_hydrothermal_alteration_in_volcanic_rocks
• https://link.springer.com/article/10.1007/s11053-024-10369-7 -> Predictive Modeling of Canadian Carbonatite-Hosted REE +/− Nb Deposits
• https://www.sciencedirect.com/science/article/pii/S0098300422001406 -> Preliminary geological mapping with convolution neural network using statistical data augmentation on a 3D model
• https://www.researchgate.net/publication/352046255_Study_of_the_Influence_of_Non-Deposit_Locations_in_Data-Driven_Mineral_Prospectivity_Mapping_A_Case_Study_on_the_Iskut_Project_in_Northwestern_British_Columbia_Canada
• https://www.researchgate.net/publication/220164155_Support_vector_machine_A_tool_for_mapping_mineral_prospectivity
• https://www.researchgate.net/publication/348111963_Support_Vector_Machine_and_Artificial_Neural_Network_Modelling_of_Orogenic_Gold_Prospectivity_Mapping_in_the_Swayze_greenstone_belt_Ontario_Canada
• PhD thesis -> https://zone.biblio.laurentian.ca/bitstream/10219/3736/1/PhD%20Thesis%20Maepa_20210603.%281%29.pdf -> Exploration targeting for gold deposits using spatial data analytics, machine learning and deep transfer learning in the Swayze and Matheson greenstone belts, Ontario, Canada
• https://data.geology.gov.yk.ca/Reference/95936#InfoTab -> Updates to the Yukon Geological Survey's mineral potential mapping methodology
• http://www.geosciencebc.com/i/pdf/SummaryofActivities2015/SoA2015_Granek.pdf -> Advanced Geoscience Targeting via Focused Machine Learning Applied to the QUEST Project Dataset, British Columbia

África Central

• https://www.researchgate.net/publication/323452014_The_Utility_of_Machine_Learning_in_Identification_of_Key_Geophysical_and_Geochemical_Datasets_A_Case_Study_in_Lithological_Mapping_in_the_Central_African_Copper_Belt
• https://www.researchgate.net/publication/334436808_Lithological_Mapping_in_the_Central_African_Copper_Belt_using_Random_Forests_and_Clustering_Strategies_for_Optimised_Results

Chile

• https://www.researchgate.net/publication/341485750_Evaluation_of_random_forest-based_analysis_for_the_gypsum_distribution_in_the_Atacama_desert

China

• https://www.researchgate.net/publication/374968979_3D_cooperative_inversion_of_airborne_magnetic_and_gravity_gradient_data_using_deep_learning_techniques - 3D cooperative inversion of airborne magnetic and gravity gradient data using deep learning techniques [UNSEEN]
• https://www.researchgate.net/publication/369919958_3D_mineral_exploration_Cu-Zn_targeting_with_multi-source_geoscience_datasets_in_the_Weilasituo-bairendaba_district_Inner_Mongolia_China
• https://www.researchgate.net/publication/350817136_3D_Mineral_Prospectivity_Mapping_Based_on_Deep_Metallogenic_Prediction_Theory_A_Case_Study_of_the_Lala_Copper_Mine_Sichuan_China
• https://www.researchgate.net/publication/336771580_3D_Mineral_Prospectivity_Mapping_with_Random_Forests_A_Case_Study_of_Tongling_Anhui_China
• https://www.sciencedirect.com/science/article/pii/S0169136823005772 -> 3D mineral prospectivity modeling in the Sanshandao goldfield, China using the convolutional neural network with attention mechanism
• https://www.sciencedirect.com/science/article/pii/S0009281924001144 -> 3D mineral prospectivity modeling using deep adaptation network transfer learning: A case study of the Xiadian gold deposit, Eastern China
• https://www.sciencedirect.com/science/article/pii/S0009281924000497 -> 3D mineral prospectivity modeling using multi-scale 3D convolution neural network and spatial attention approaches
• https://www.researchgate.net/publication/366201930_3D_Quantitative_Metallogenic_Prediction_of_Indium-Rich_Ore_Bodies_in_the_Dulong_Sn-Zn_Polymetallic_Deposit_Yunnan_Province_SW_China
• https://www.researchgate.net/publication/329600793_A_combined_approach_using_spatially-weighted_principal_components_analysis_and_wavelet_transformation_for_geochemical_anomaly_mapping_in_the_Dashui_ore-concentration_district_Central_China
• https://www.researchgate.net/publication/349034539_A_Comparative_Study_of_Machine_Learning_Models_with_Hyperparameter_Optimization_Algorithm_for_Mapping_Mineral_Prospectivity
• https://www.researchgate.net/publication/354132594_A_Convolutional_Neural_Network_of_GoogLeNet_Applied_in_Mineral_Prospectivity_Prediction_Based_on_Multi-source_Geoinformation
• https://www.researchgate.net/publication/369865076_A_deep-learning-based_mineral_prospectivity_modeling_framework_and_workflow_in_prediction_of_porphyry-epithermal_mineralization_in_the_Duolong_Ore_District_Tibet
• https://www.researchgate.net/publication/374982967_A_Framework_for_Data-Driven_Mineral_Prospectivity_Mapping_with_Interpretable_Machine_Learning_and_Modulated_Predictive_Modeling
• https://www.sciencedirect.com/science/article/pii/S0169136824002026 -> A Global-Local collaborative approach to quantifying spatial non-stationarity in three-dimensional mineral prospectivity modeling
• https://link.springer.com/article/10.1007/s11053-024-10344-2 -> A Heterogeneous Graph Construction Method for Mineral Prospectivity Mapping [UNSEEN]
• https://www.researchgate.net/publication/353421842_A_hybrid_logistic_regression_gene_expression_programming_model_and_its_application_to_mineral_prospectivity_mapping
• https://www.researchgate.net/publication/375764940_A_lightweight_convolutional_neural_network_with_end-to-end_learning_for_three-dimensional_mineral_prospectivity_modeling_A_case_study_of_the_Sanhetun_Area_Heilongjiang_Province_Northeastern_China
• https://www.researchgate.net/publication/339821823_A_Monte_Carlo-based_framework_for_risk-return_analysis_in_mineral_prospectivity_mapping
• https://www.researchgate.net/publication/373715610_A_Multimodal_Learning_Framework_for_Comprehensive_3D_Mineral_Prospectivity_Modeling_with_Jointly_Learned_Structure-Fluid_Relationships
• https://www.sciencedirect.com/science/article/pii/S0169136824001343 -> A novel hybrid ensemble model for mineral prospectivity prediction: A case study in the Malipo W-Sn mineral district, Yunnan Province, China
• https://www.researchgate.net/publication/347344551_A_positive_and_unlabeled_learning_algorithm_for_mineral_prospectivity_mapping
• https://www.researchgate.net/publication/335036019_An_Autoencoder-Based_Dimensionality_Reduction_Algorithm_for_Intelligent_Clustering_of_Mineral_Deposit_Data
• https://www.researchgate.net/publication/363696083_An_Integrated_Framework_for_Data-Driven_Mineral_Prospectivity_Mapping_Using_Bagging-Based_Positive_Unlabeled_Learning_and_Bayesian_Cost-Sensitive_Logistic_Regression
• https://link.springer.com/article/10.1007/s11053-024-10349-x -> An Uncertainty-Quantification Machine Learning Framework for Data-Driven Three-Dimensional Mineral Prospectivity Mapping
• https://link.springer.com/article/10.1007/s11004-023-10076-8 - An Interpretable Graph Attention Network for Mineral Prospectivity Mapping
• https://www.researchgate.net/publication/332751556_Application_of_hierarchical_clustering_singularity_mapping_and_Kohonen_neural_network_to_identify_Ag-Au-Pb-Zn_polymetallic_mineralization_associated_geochemical_anomaly_in_Pangxidong_district
• https://www.mdpi.com/2075-163X/14/9/945 -> Application of Machine Learning to Characterize Metallogenic Potential Based on Trace Elements of Zircon: A Case Study of the Tethyan Domain
• https://www.researchgate.net/publication/339096362_Application_of_nonconventional_mineral_exploration_techniques_case_studies
• https://www.researchgate.net/publication/325702993_Assessment_of_Geochemical_Anomaly_Uncertainty_Through_Geostatistical_Simulation_and_Singularity_Analysis
• https://www.researchgate.net/publication/368586826_Bagging-based_Positive-Unlabeled_Data_Learning_Algorithm_with_Base_Learners_Random_Forest_and_XGBoost_for_3D_Exploration_Targeting_in_the_Kalatongke_District_Xinjiang_China
• https://link.springer.com/article/10.1007/s11004-024-10153-6 -> Causal Discovery and Deep Learning Algorithms for Detecting Geochemical Patterns Associated with Gold-Polymetallic Mineralization: A Case Study of the Edongnan Region [UNSEEN]
• https://www.sciencedirect.com/science/article/pii/S0169136824001409 -> CNN-Transformers for mineral prospectivity mapping in the Maodeng–Baiyinchagan area, Southern Great Xing'an Range
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• https://www.researchgate.net/publication/352703015_Data-driven_based_logistic_function_and_prediction-area_plot_for_mineral_prospectivity_mapping_a_case_study_from_the_eastern_margin_of_Qinling_orogenic_belt_central_China
• https://www.sciencedirect.com/science/article/abs/pii/S0012825218306123 -> Deep learning and its application in geochemical mapping
• https://www.frontiersin.org/articles/10.3389/feart.2024.1308426/full -> Deep gold prospectivity modeling in the Jiaojia gold belt, Jiaodong Peninsula, eastern China using machine learning of geometric and geodynamic variables
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• https://www.sciencedirect.com/science/article/abs/pii/S0375674221001370 -> Distinguishing IOCG and IOA deposits via random forest algorithm based on magnetite composition
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• https://www.researchgate.net/publication/360333702_Ensemble_learning_models_with_a_Bayesian_optimization_algorithm_for_mineral_prospectivity_mapping
• https://www.researchgate.net/publication/267927676_Evaluation_of_uncertainty_in_mineral_prospectivity_mapping_due_to_missing_evidence_A_case_study_with_skarn-type_Fe_deposits_in_Southwestern_Fujian_Province_China
• https://www.mdpi.com/2075-163X/14/5/492 ->Exploration Vectors and Indicators Extracted by Factor Analysis and Association Rule Algorithms at the Lintan Carlin-Type Gold Deposit, Youjiang Basin, China
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• https://www.researchgate.net/publication/359714254_Geochemical_characterization_of_the_Central_Mineral_Belt_U_Cu_Mo_V_mineralization_Labrador_Canada_Application_of_unsupervised_machine-learning_for_evaluation_of_IOCG_and_affiliated_mineral_potential
• https://www.researchgate.net/publication/350788828_Geochemically_Constrained_Prospectivity_Mapping_Aided_by_Unsupervised_Cluster_Analysis
• https://www.researchgate.net/publication/267927506_GIS-based_mineral_potential_modeling_by_advanced_spatial_analytical_methods_in_the_southeastern_Yunnan_mineral_district_China
• https://www.researchgate.net/publication/380190183_Geologically_Constrained_Convolutional_Neural_Network_for_Mineral_Prospectivity_Mapping
• https://www.researchgate.net/publication/332997161_GNER_A_Generative_Model_for_Geological_Named_Entity_Recognition_Without_Labeled_Data_Using_Deep_Learning
• https://www.researchgate.net/publication/307011381_Identification_and_mapping_of_geochemical_patterns_and_their_significance_for_regional_metallogeny_in_the_southern_Sanjiang_China
• https://link.springer.com/article/10.1007/s11053-024-10334-4 -> Identification of Geochemical Anomalies Using an End-to-End Transformer
• https://www.researchgate.net/publication/359627130_Identification_of_ore-finding_targets_using_the_anomaly_components_of_ore-forming_element_associations_extracted_by_SVD_and_PCA_in_the_Jiaodong_gold_cluster_area_Eastern_China
• https://www.researchgate.net/publication/282621670_Identifying_geochemical_anomalies_associated_with_Au-Cu_mineralization_using_multifractal_and_artificial_neural_network_models_in_the_Ningqiang_district_Shaanxi_China
• https://www.sciencedirect.com/science/article/abs/pii/S0375674224000943 -> Integrate physics-driven dynamics simulation with data-driven machine learning to predict potential targets in maturely explored orefields: A case study in Tongguangshan orefield, Tongling, China
• https://www.researchgate.net/publication/329299202_Integrating_sequential_indicator_simulation_and_singularity_analysis_to_analyze_uncertainty_of_geochemical_anomaly_for_exploration_targeting_of_tungsten_polymetallic_mineralization_Nanling_belt_South_
• https://www.sciencedirect.com/science/article/abs/pii/S0883292724001987 -> Integrating soil geochemistry and machine learning for enhanced mineral exploration at the dayu gold deposit, south China block
• https://www.mdpi.com/2071-1050/15/13/10269 -> Intelligent Identification and Prediction Mineral Resources Deposit Based on Deep Learning
• https://link.springer.com/article/10.1007/s11053-024-10396-4 -> Interpretable SHAP Model Combining Meta-learning and Vision Transformer for Lithology Classification Using Limited and Unbalanced Drilling Data in Well Logging
• https://www.researchgate.net/publication/358555996_Learning_3D_mineral_prospectivity_from_3D_geological_models_using_convolutional_neural_networks_Application_to_a_structure-controlled_hydrothermal_gold_deposit
• https://www.researchgate.net/publication/352476625_Machine_Learning-Based_3D_Modeling_of_Mineral_Prospectivity_Mapping_in_the_Anqing_Orefield_Eastern_China
• https://www.researchgate.net/publication/331575655_Mapping_Geochemical_Anomalies_Through_Integrating_Random_Forest_and_Metric_Learning_Methods
• https://www.researchgate.net/publication/229399579_Mapping_geochemical_singularity_using_multifractal_analysis_Application_to_anomaly_definition_on_stream_sediments_data_from_Funin_Sheet_Yunnan_China
• https://www.researchgate.net/publication/328255422_Mapping_mineral_prospectivity_through_big_data_analytics_and_a_deep_learning_algorithm
• https://www.researchgate.net/publication/334106787_Mapping_Mineral_Prospectivity_via_Semi-supervised_Random_Forest
• https://www.researchgate.net/publication/236270466_Mapping_of_district-scale_potential_targets_using_fractal_models
• https://www.researchgate.net/publication/357584076_Mapping_prospectivity_for_regolith-hosted_REE_deposits_via_convolutional_neural_network_with_generative_adversarial_network_augmented_data
• https://www.researchgate.net/publication/328623280_Maximum_Entropy_and_Random_Forest_Modeling_of_Mineral_Potential_Analysis_of_Gold_Prospectivity_in_the_Hezuo-Meiwu_District_West_Qinling_Orogen_China
• https://www.sciencedirect.com/science/article/pii/S016913682400163X -> Metallogenic prediction based on fractal theory and machine learning in Duobaoshan Area, Heilongjiang Province
• https://www.sciencedirect.com/science/article/pii/S0169136824003810 -> Mineral prospectivity mapping susceptibility evaluation based on interpretable ensemble learning
• https://link.springer.com/article/10.1007/s11053-024-10386-6 -> Mineral Prospectivity Mapping Based on Spatial Feature Classification with Geological Map Knowledge Graph Embedding: Case Study of Gold Ore Prediction at Wulonggou, Qinghai Provinc
• https://www.researchgate.net/publication/235443301_Mineral_potential_mapping_in_a_frontier_region
• https://www.researchgate.net/publication/235443302_Mineral_potential_mapping_in_frontier_regions_A_Mongolian_case_study
• https://www.researchgate.net/publication/369104190_Mineral_Prospectivity_Mapping_Using_Attention-based_Convolutional_Neural_Network
• https://www.nature.com/articles/s41598-024-73357-0 -> Mineral prospectivity prediction based on convolutional neural network and ensemble learning
• https://www.researchgate.net/publication/329037175_Mineral_prospectivity_analysis_for_BIF_iron_deposits_A_case_study_in_the_Anshan-Benxi_area_Liaoning_province_North-East_China
• https://link.springer.com/article/10.1007/s11053-024-10335-3 -> Mineral Prospectivity Prediction Based on Self-Supervised Contrastive Learning and Geochemical Data: A Case Study of the Gold Deposit in the Malanyu District, Hebei Province, China [USEEN]
• https://www.researchgate.net/publication/377694139_Manganese_mineral_prospectivity_based_on_deep_convolutional_neural_networks_in_Songtao_of_northeastern_Guizhou
• https://www.researchgate.net/publication/ 351649498_Mineral_Prospectivity_Mapping_based_on_Isolation_Forest_and_Random_Forest_Implication_for_the_Existence_of_Spatial_Signature_of_Mineralization_in_Outliers
• https://www.researchgate.net/publication/358528670_Mineral_Prospectivity_Mapping_Based_on_Wavelet_Neural_Network_and_Monte_Carlo_Simulations_in_the_Nanling_W-Sn_Metallogenic_Province
• https://www.researchgate.net/publication/352983697_Mineral_prospectivity_mapping_by_deep_learning_method_in_Yawan-Daqiao_area_Gansu
• https://www.researchgate.net/publication/367106018_Mineral_Prospectivity_Mapping_of_Porphyry_Copper_Deposits_Based_on_Remote_Sensing_Imagery_and_Geochemical_Data_in_the_Duolong_Ore_District_Tibet - Mineral Prospectivity Mapping of Porphyry Copper Deposits Based on Remote Sensing Imagery and Geochemical Data in the Duolong Ore District, Tibet
• https://www.researchgate.net/publication/355749736_Mineral_prospectivity_mapping_using_a_joint_singularity-based_weighting_method_and_long_short-term_memory_network
• https://www.researchgate.net/publication/369104190_Mineral_Prospectivity_Mapping_Using_Attention-based_Convolutional_Neural_Network
• https://www.researchgate.net/publication/365434839_Mineral_Prospectivity_Mapping_Using_Deep_Self-Attention_Model
• https://www.researchgate.net/publication/379674196_Mineral_prospectivity_mapping_using_knowledge_embedding_and_explainable_ensemble_learning_A_case_study_of_the_Keeryin_ore_concentration_in_Sichuan_China
• https://www.researchgate.net/publication/350817877_Mineral_Prospectivity_Prediction_via_Convolutional_Neural_Networks_Based_on_Geological_Big_Data
• https://www.researchgate.net/publication/338871759_Modeling-based_mineral_system_approach_to_prospectivity_mapping_of_stratabound_hydrothermal_deposits_A_case_study_of_MVT_Pb-Zn_deposits_in_the_Huayuan_area_northwestern_Hunan_Province_China
• https://www.sciencedirect.com/science/article/pii/S0169136824003172 -> New insights into the metallogenic genesis of the Xiadian Au deposit, Jiaodong Peninsula, Eastern China: Constraints from integrated rutile in-situ geochemical analysis and machine learning discrimination
• https://www.researchgate.net/publication/332547136_Prospectivity_Mapping_for_Porphyry_Cu-Mo_Mineralization_in_the_Eastern_Tianshan_Xinjiang_Northwestern_China
• https://www.sciencedirect.com/science/article/pii/S0169136824001823 -> Quantitative prediction methods and applications of digital ore deposit models
• https://www.researchgate.net/publication/344303914_Random-Drop_Data_Augmentation_of_Deep_Convolutional_Neural_Network_for_Mineral_Prospectivity_Mapping
• https://www.researchgate.net/publication/371044606_Supervised_Mineral_Prospectivity_Mapping_via_Class-Balanced_Focal_Loss_Function_on_Imbalanced_Geoscience_DatasetsSupervised Mineral Prospectivity Mapping via Class-Balanced Focal Loss Function on Imbalanced Geoscience Datasets
• https://www.researchgate.net/publication/361520562_Recognizing_Multivariate_Geochemical_Anomalies_Related_to_Mineralization_by_Using_Deep_Unsupervised_Graph_Learning
• https://www.sciencedirect.com/science/article/pii/S0169136824003937 -> Semi-supervised graph convolutional networks for integrating continuous and binary evidential layers for mineral exploration targeting
• https://www.researchgate.net/publication/371044606_Supervised_Mineral_Prospectivity_Mapping_via_Class-Balanced_Focal_Loss_Function_on_Imbalanced_Geoscience_Datasets
• https://www.researchgate.net/publication/360028637_Three-Dimensional_Mineral_Prospectivity_Mapping_by_XGBoost_Modeling_A_Case_Study_of_the_Lannigou_Gold_Deposit_China
• https://link.springer.com/article/10.1007/s11053-024-10387-5 - Toward Data-Driven Mineral Prospectivity Mapping from Remote Sensing Data Using Deep Forest Predictive Model
• https://www.researchgate.net/publication/361589587_Unlabeled_Sample_Selection_for_Mineral_Prospectivity_Mapping_by_Semi-supervised_Support_Vector_Machine
• https://www.researchgate.net/publication/343515866_Using_deep_variational_autoencoder_networks_for_recognizing_geochemical_anomalies
• https://link.springer.com/article/10.1007/s11004-024-10151-8 -> Using Three-dimensional Modeling and Random Forests to Predict Deep Ore Potentials: A Case Study on Xiongcun Porphyry Copper–Gold Deposit in Tibet, China
• https://www.researchgate.net/publication/361194407_Visual_Interpretable_Deep_Learning_Algorithm_for_Geochemical_Anomaly_Recognition

Egito

• https://www.researchgate.net/publication/340084035_Reliability_of_using_ASTER_data_in_lithologic_mapping_and_alteration_mineral_detection_of_the_basement_complex_of_West_Berenice_Southeastern_Desert_Egypt

Inglaterra

• https://www.researchgate.net/publication/342339753_A_machine_learning_approach_to_tungsten_prospectivity_modelling_using_knowledge-driven_feature_extraction_and_model_confidence
• https://www.researchgate.net/project/Enhancing-the-Geological-Understanding-of-SW-England-Using-Machine-Learning-Algorithms

Eritreia

• https://www.researchgate.net/publication/349158008_Mapping_gold_mineral_prospectivity_based_on_weights_of_evidence_method_in_southeast_Asmara_Eritrea

Finlândia

• https://www.researchgate.net/publication/360661926_Target-scale_prospectivity_modeling_for_gold_mineralization_within_the_Rajapalot_Au-Co_project_area_in_northern_Fennoscandian_Shield_Finland_Part_2_Application_of_self-organizing_maps_and_artificial_n
• https://www.sciencedirect.com/science/article/pii/S0169136824004037 -> Addressing imbalanced data for machine learning based mineral prospectivity mapping

Finlândia

• https://publications.csiro.au/publications/#publication/PIcsiro:EP146125/SQmineral%20prospectivity/RP1/RS50/RORECENT/STsearch-by-keyword/LISEA/RI12/RT26 -> A novel spatial analysis approach for assessing regional-scale mineral prospectivity In Northern Finland
• https://www.researchgate.net/publication/332352805_Boosting_for_Mineral_Prospectivity_Modeling_A_New_GIS_Toolbox
• https://www.researchgate.net/publication/324517415_Can_boosting_boost_minimal_invasive_exploration_targeting
• https://www.researchgate.net/publication/248955109_Combined_conceptualempirical_prospectivity_mapping_for_orogenic_gold_in_the_northern_Fennoscandian_Shield_Finland
• https://www.researchgate.net/publication/283451958_Data-driven_logistic-based_weighting_of_geochemical_and_geological_evidence_layers_in_mineral_prospectivity_mapping
• https://www.researchgate.net/publication/320280611_Evaluation_of_boosting_algorithms_for_prospectivity_mapping
• https://www.researchgate.net/publication/298297988_Fuzzy_logic_data_integration_technique_used_as_a_nickel_exploration_tool
• https://www.researchgate.net/publication/259372191_Gravity_data_in_regional_scale_3D_and_gold_prospectivity_modelling_-_example_from_the_Central_Lapland_greenstone_belt_northern_Finland
• https://www.researchgate.net/publication/315381587_Introduction_to_the_special_issue_GIS-based_mineral_potential_targeting
• https://www.researchgate.net/publication/320709733_Knowledge-driven_prospectivity_model_for_Iron_oxide-Cu-Au_IOCG_deposits_in_northern_Finland
• https://tupa.gtk.fi/raportti/arkisto/57_2021.pdf -> Mineral Prospectivity and Exploration Targeting MinProXT 2021 Webinar - paper compilation
• https://tupa.gtk.fi/raportti/arkisto/29_2023.pdf -> Mineral Prospectivity and Exploration Targeting MinProXT 2022 Webinar - paper compilation
• https://www.researchgate.net/publication/312180531_Optimizing_a_Knowledge-driven_Prospectivity_Model_for_Gold_Deposits_Within_Perapohja_Belt_Northern_Finland
• https://www.researchgate.net/publication/320703774_Prospectivity_Models_for_Volcanogenic_Massive_Sulfide_Deposits_VMS_in_Northern_Finland
• https://www.researchgate.net/publication/280875727_Receiver_operating_characteristics_ROC_as_validation_tool_for_prospectivity_models_-_A_magmatic_Ni-Cu_case_study_from_the_Central_Lapland_Greenstone_Belt_Northern_Finland
• https://www.researchgate.net/publication/332298116_Scalability_of_the_Mineral_Prospectivity_Modelling_-_An_orogenic_gold_case_study_from_northern_Finland
• https://www.researchgate.net/publication/251786465_Spatial_data_analysis_as_a_tool_for_mineral_prospectivity_mapping
• https://www.researchgate.net/publication/331006924_Unsupervised_clustering_and_empirical_fuzzy_memberships_for_mineral_prospectivity_modelling

Gana

• https://www.researchgate.net/publication/227256267_Application_of_Data-Driven_Evidential_Belief_Functions_to_Prospectivity_Mapping_for_Aquamarine-Bearing_Pegmatites_Lundazi_District_Zambia
• https://www.researchgate.net/publication/226842511_Mapping_of_prospectivity_and_estimation_of_number_of_undiscovered_prospects_for_lode_gold_southwestern_Ashanti_Belt_Ghana
• https://www.researchgate.net/publication/233791624_Spatial_association_of_gold_deposits_with_remotely_-_sensed_faults_South_Ashanti_belt_Ghana

Groenlândia

• https://www.researchgate.net/publication/360970965_Identification_of_Radioactive_Mineralized_Lithology_and_Mineral_Prospectivity_Mapping_Based_on_Remote_Sensing_in_High-Latitude_Regions_A_Case_Study_on_the_Narsaq_Region_of_Greenland

Índia

• https://www.researchgate.net/publication/372636338_Unsupervised_machine_learning_based_prospectivity_analysis_of_NW_and_NE_India_for_carbonatite-alkaline_complex-related_REE_deposits

Indonésia

• https://www.researchgate.net/publication/263542819_Regional-Scale_Geothermal_Prospectivity_Mapping_in_West_Java_Indonesia_by_Data-driven_Evidential_Belief_Functions

Irã

• https://www.researchgate.net/publication/325697373_A_comparative_analysis_of_artificial_neural_network_ANN_wavelet_neural_network_WNN_and_support_vector_machine_SVM_data-driven_models_to_mineral_potential_mapping_for_copper_mineralizations_in_the_Shah
• https://www.researchgate.net/publication/358507255_A_Comparative_Study_of_Convolutional_Neural_Networks_and_Conventional_Machine_Learning_Models_for_Lithological_Mapping_Using_Remote_Sensing_Data
• https://www.researchgate.net/publication/351750324_A_data_augmentation_approach_to_XGboost-based_mineral_potential_mapping_An_example_of_carbonate-hosted_Zn_Pb_mineral_systems_of_Western_Iran
• https://www.researchgate.net/publication/336471932_A_knowledge-guided_fuzzy_inference_approach_for_integrating_geophysics_geochemistry_and_geology_data_in_deposit-scale_porphyry_copper_targeting_Saveh-Iran
• https://www.researchgate.net/publication/348500913_A_new_strategy_for_spatial_predictive_mapping_of_mineral_prospectivity
• https://www.researchgate.net/publication/348482539_A_new_strategy_for_spatial_predictive_mapping_of_mineral_prospectivity_Automated_hyperparameter_tuning_of_random_forest_approach
• https://www.researchgate.net/publication/352251016_A_simulation-based_framework_for_modulating_the_effects_of_subjectivity_in_greenfield_Mineral_Prospectivity_Mapping_with_geochemical_and_geological_data
• https://www.researchgate.net/publication/296638839_An_AHP-TOPSIS_Predictive_Model_for_District-Scale_Mapping_of_Porphyry_Cu-Au_Potential_A_Case_Study_from_Salafchegan_Area_Central_Iran
• https://www.researchgate.net/publication/278029106_Application_of_Discriminant_Analysis_and_Support_Vector_Machine_in_Mapping_Gold_Potential_Areas_for_Further_Drilling_in_the_Sari-Gunay_Gold_Deposit_NW_Iran
• https://www.researchgate.net/publication/220164381_Application_of_geochemical_zonality_coefficients_in_mineral_prospectivity_mapping
• https://www.researchgate.net/publication/330359897_Application_of_hybrid_AHP-TOPSIS_method_for_prospectivity_modeling_of_Cu_porphyry_in_Varzaghan_district_Iran
• https://www.researchgate.net/publication/356872819_Application_of_self-organizing_map_SOM_and_K-means_clustering_algorithms_for_portraying_geochemical_anomaly_patterns_in_Moalleman_district_NE_Iran
• https://www.researchgate.net/publication/258505300_Application_of_staged_factor_analysis_and_logistic_function_to_create_a_fuzzy_stream_sediment_geochemical_evidence_layer_for_mineral_prospectivity_mapping
• https://www.researchgate.net/publication/358567148_Applications_of_data_augmentation_in_mineral_prospectivity_prediction_based_on_convolutional_neural_networks
• https://www.researchgate.net/publication/353761696_Assessing_the_effects_of_mineral_systems-derived_exploration_targeting_criteria_for_Random_Forests-based_predictive_mapping_of_mineral_prospectivity_in_Ahar-Arasbaran_area_Iran
• https://www.researchgate.net/publication/270586282_Data-Driven_Index_Overlay_and_Boolean_Logic_Mineral_Prospectivity_Modeling_in_Greenfields_Exploration
• https://www.researchgate.net/publication/356660905_Deep_GMDH_Neural_Networks_for_Predictive_Mapping_of_Mineral_Prospectivity_in_Terrains_Hosting_Few_but_Large_Mineral_Deposits
• https://www.researchgate.net/publication/317240761_Enhancement_and_Mapping_of_Weak_Multivariate_Stream_Sediment_Geochemical_Anomalies_in_Ahar_Area_NW_Iran
• https://www.sciencedirect.com/science/article/pii/S0009281924001223 -> Enhancing training performance of convolutional neural network algorithm through an autoencoder-based unsupervised labeling framework for mineral exploration targeting
• https://www.researchgate.net/publication/356580903_Evidential_data_integration_to_produce_porphyry_Cu_prospectivity_map_using_a_combination_of_knowledge_and_data_driven_methods
• https://research-repository.uwa.edu.au/en/publications/exploration-feature-selection-applied-to-hybrid-data-integration-Exploration feature selection applied to hybrid data integrationmodeling: Targeting copper-gold potential in central
• https://www.researchgate.net/publication/333199619_Incorporation_of_principal_component_analysis_geostatistical_interpolation_approaches_and_frequency-space-based_models_for_portraying_the_Cu-Au_geochemical_prospects_in_the_Feizabad_district_NW_Iran
• https://www.researchgate.net/publication/351965039_Intelligent_geochemical_exploration_modeling_using_multiclass_support_vector_machine_and_integration_it_with_continuous_genetic_algorithm_in_Gonabad_region_Khorasan_Razavi_Iran
• https://www.researchgate.net/publication/310658663_Multifractal_interpolation_and_spectrum-area_fractal_modeling_of_stream_sediment_geochemical_data_Implications_for_mapping_exploration_targets
• https://www.researchgate.net/publication/267635150_Multivariate_regression_analysis_of_lithogeochemical_data_to_model_subsurface_mineralization_A_case_study_from_the_Sari_Gunay_epithermal_gold_deposit_NW_Iran
• https://www.researchgate.net/publication/330129457_Performance_evaluation_of_RBF-_and_SVM-based_machine_learning_algorithms_for_predictive_mineral_prospectivity_modeling_integration_of_S-A_multifractal_model_and_mineralization_controls
• https://www.researchgate.net/publication/353982380_Porphyry_Cu-Au_prospectivity_modelling_using_semi-supervised_learning_algorithm_in_Dehsalm_district_eastern_Iran_In_Farsi_with_extended_English_abstract
• https://www.researchgate.net/publication/320886789_Prospectivity_analysis_of_orogenic_gold_deposits_in_Saqez-Sardasht_Goldfield_Zagros_Orogen_Iran
• https://www.researchgate.net/publication/361529867_Prospectivity_mapping_of_orogenic_lode_gold_deposits_using_fuzzy_models_a_case_study_of_Saqqez_area_NW_of_Iran
• https://www.researchgate.net/publication/361717490_Quantifying_Uncertainties_Linked_to_the_Diversity_of_Mathematical_Frameworks_in_Knowledge-Driven_Mineral_Prospectivity_Mapping
• https://www.researchgate.net/publication/374730424_Recognition_of_mineralization-related_anomaly_patterns_through_an_autoencoder_neural_network_for_mineral_exploration_targeting
• https://www.researchgate.net/publication/349957803_Regional-Scale_Mineral_Prospectivity_Mapping_Support_Vector_Machines_and_an_Improved_Data-Driven_Multi-criteria_Decision-Making_Technique
• https://www.researchgate.net/publication/339153591_Sensitivity_analysis_of_prospectivity_modeling_to_evidence_maps_Enhancing_success_of_targeting_for_epithermal_gold_Takab_district_NW_Iran
• https://www.researchgate.net/publication/321076980_Spatial_analyses_of_exploration_evidence_data_to_model_skarn-type_copper_prospectivity_in_the_Varzaghan_district_NW_Iran
• https://www.researchgate.net/publication/304904242_Stepwise_regression_for_recognition_of_geochemical_anomalies_Case_study_in_Takab_area_NW_Iran
• https://www.researchgate.net/publication/350423220_Supervised_mineral_exploration_targeting_and_the_challenges_with_the_selection_of_deposit_and_non-deposit_sites_thereof
• https://www.sciencedirect.com/science/article/pii/S0009281924000801 -> Targeting porphyry Cu deposits in the Chahargonbad region of Iran: A joint application of deep belief networks and random forest techniques
• https://www.researchgate.net/publication/307874730_The_use_of_decision_tree_induction_and_artificial_neural_networks_for_recognizing_the_geochemical_distribution_patterns_of_LREE_in_the_Choghart_deposit_Central_Iran
• https://www.researchsquare.com/article/rs-4760956/v1 -> Uncertainty reduction with Hyperparameter Optimization in mineral prospectivity mapping: A Regularized Artificial Neural Network approach [UNSEEN]

Irlanda

• https://www.gsi.ie/en-ie/programmes-and-projects/tellus/activities/tellus-product-development/mineral-prospectivity/Pages/default.aspx - > NW Midlands Mineral Prospectivity Mapping

Índia

• https://www.researchgate.net/publication/226092981_A_Hybrid_Neuro-Fuzzy_Model_for_Mineral_Potential_Mapping
• https://www.researchgate.net/publication/225328359_A_Hybrid_Fuzzy_Weights-of-Evidence_Model_for_Mineral_Potential_Mapping
• https://www.researchgate.net/publication/227221497_Artificial_Neural_Networks_for_Mineral-Potential_Mapping_A_Case_Study_from_Aravalli_Province_Western_India
• https://www.researchgate.net/publication/222050039_Bayesian_network_classifiers_for_mineral_potential_mapping
• https://www.researchgate.net/publication/355397149_Gold_Prospectivity_Mapping_in_the_Sonakhan_Greenstone_Belt_Central_India_A_Knowledge-Driven_Guide_for_Target_Delineation_in_a_Region_of_Low_Exploration_Maturity
• https://www.researchgate.net/publication/272092276_Extended_Weights-of-Evidence_Modelling_for_Predictive_Mapping_of_Base_Metal_Deposit_Potential_in_Aravalli_Province_Western_India
• https://www.researchgate.net/publication/226193283_Knowledge-Driven_and_Data-Driven_Fuzzy_Models_for_Predictive_Mineral_Potential_Mapping
• https://www.researchgate.net/publication/238027981_SVM-based_base-metal_prospectivity_modeling_of_the_Aravalli_Orogen_Northwestern_India

Coréia

• https://www.researchgate.net/publication/382131746_Domain_Adaptation_from_Drilling_to_Geophysical_Data_for_Mineral_Exploration

Noruega

• https://www.mdpi.com/2075-163X/9/2/131/htm - Prospectivity Mapping of Mineral Deposits in Northern Norway Using Radial Basis Function Neural Networks

Coréia do Sul

• https://www.researchgate.net/publication/221911782_Application_of_Artificial_Neural_Network_for_Mineral_Potential_Mapping
• https://www.researchgate.net/publication/359861043_Rock_Classification_in_a_Vanadiferous_Titanomagnetite_Deposit_Based_on_Supervised_Machine_Learning#fullTextFileContent Rock Classification in a Vanadiferous Titanomagnetite Deposit Based on Supervised Machine Learning

Phillipines

• https://www.researchgate.net/publication/359632307_A_Geologically_Constrained_Variational_Autoencoder_for_Mineral_Prospectivity_Mapping
• https://www.researchgate.net/publication/263174923_Application_of_Mineral_Exploration_Models_and_GIS_to_Generate_Mineral_Potential_Maps_as_Input_for_Optimum_Land-Use_Planning_in_the_Philippines
• https://www.researchgate.net/publication/267927677_Data-driven_predictive_mapping_of_gold_prospectivity_Baguio_district_Philippines_Application_of_Random_Forests_algorithm
• https://www.researchgate.net/publication/276271833_Data-Driven_Predictive_Modeling_of_Mineral_Prospectivity_Using_Random_Forests_A_Case_Study_in_Catanduanes_Island_Philippines
• https://www.researchgate.net/publication/209803275_Evidential_belief_functions_for_data-driven_geologically_constrained_mapping_of_gold_potential_Baguio_district_Philippines
• https://www.researchgate.net/publication/241001432_Geologically_Constrained_Probabilistic_Mapping_of_Gold_Potential_Baguio_District_Philippines
• https://www.researchgate.net/publication/263724277_Geologically_Constrained_Fuzzy_Mapping_of_Gold_Mineralization_Potential_Baguio_District_Philippines
• https://www.researchgate.net/publication/229641286_Improved_Wildcat_Modelling_of_Mineral_Prospectivity
• https://www.researchgate.net/publication/238447208_Logistic_Regression_for_Geologically_Constrained_Mapping_of_Gold_Potential_Baguio_District_Philippines
• https://www.researchgate.net/publication/248977334_Mineral_imaging_with_Landsat_TM_data_for_hydrothermal_alteration_mapping_in_heavily-vegetated_terrane​​​​​​
• https://www.researchgate.net/publication/356546133_Mineral_Prospectivity_Mapping_via_Gated_Recurrent_Unit_Model
• https://www.researchgate.net/publication/267640864_Random_forest_predictive_modeling_of_mineral_prospectivity_with_small_number_of_prospects_and_data_with_missing_values_in_Abra_Philippines
• https://www.researchgate.net/publication/3931975_Remote_detection_of_vegetation_stress_for_mineral_exploration
• https://www.researchgate.net/publication/263422015_Where_Are_Porphyry_Copper_Deposits_Spatially_Localized_A_Case_Study_in_Benguet_Province_Philippines
• https://www.researchgate.net/publication/233488614_Wildcat_mapping_of_gold_potential_Baguio_District_Philippines
• https://www.researchgate.net/publication/226982180_Weights_of_Evidence_Modeling_of_Mineral_Potential_A_Case_Study_Using_Small_Number_of_Prospects_Abra_Philippines

Rússia

• https://www.researchgate.net/publication/358431343_Application_of_Maximum_Entropy_for_Mineral_Prospectivity_Mapping_in_Heavily_Vegetated_Areas_of_Greater_Kurile_Chain_with_Landsat_8_Data
• https://www.researchgate.net/publication/354000754_Mineral_Prospectivity_Mapping_for_Forecasting_Gold_Deposits_in_the_Central_Kolyma_Region_North-East_Russia

África do Sul

• https://www.researchgate.net/publication/359294267_Data-driven_multi-index_overlay_gold_prospectivity_mapping_using_geophysical_and_remote_sensing_datasets
• https://link.springer.com/article/10.1007/s11053-024-10390-w -> Mineral Reconnaissance Through Scientific Consensus: First National Prospectivity Maps for PGE–Ni–Cu–Cr and Witwatersrand-type Au Deposits in South Africa
• https://www.researchgate.net/publication/361526053_Mineral_prospectivity_mapping_of_gold-base_metal_mineralisation_in_the_Sabie-Pilgrim%27s_Rest_area_Mpumalanga_Province_South_Africa
• https://www.researchgate.net/publication/264296137_PREDICTIVE_BEDROCK_AND_MINERAL_PROSPECTIVITY_MAPPING_IN_THE_GIYANI_GREENSTONE_BELT_SOUTH_AFRICA
• https://www.researchgate.net/publication/268196204_Predictive_mapping_of_prospectivity_for_orogenic_gold_Giyani_greenstone_belt_South_Africa

Espanha

• https://www.researchgate.net/publication/225656353_Deriving_Optimal_Exploration_Target_Zones_on_Mineral_Prospectivity_Maps
• https://www.researchgate.net/publication/222198648_Knowledge-guided_data-driven_evidential_belief_modeling_of_mineral_prospectivity_in_Cabo_de_Gata_SE_Spain
• https://www.researchgate.net/publication/356639977_Machine_learning_models_for_Hg_prospecting_in_the_Almaden_mining_district
• https://www.researchgate.net/publication/43165602_Methodology_for_deriving_optimal_exploration_target_zones
• https://www.researchgate.net/publication/263542579_Optimal_Exploration_Target_Zones
• https://www.researchgate.net/publication/222892103_Optimal_field_sampling_for_targeting_minerals_using_hyperspectral_data
• https://www.researchgate.net/publication/271671416_Predictive_modelling_of_gold_potential_with_the_integration_of_multisource_information_based_on_random_forest_a_case_study_on_the_Rodalquilar_area_Southern_Spain

Sudão

• https://link.springer.com/article/10.1007/s11053-024-10387-5 -> Toward Data-Driven Mineral Prospectivity Mapping from Remote Sensing Data Using Deep Forest Predictive Model [UNSEEN]

Suécia

• https://www.researchgate.net/publication/259128115_Biogeochemical_expression_of_rare_earth_element_and_zirconium_mineralization_at_Norra_Karr_Southern_Sweden
• https://www.researchgate.net/publication/260086862_COMPARISION_OF_VMS_PROSPECTIVITY_MAPPING_BY_EBF_AND_WOFE_MODELING_THE_SKELLEFTE_DISTRICT_SWEDEN
• https://www.researchgate.net/publication/336086368_GIS-based_mineral_system_approach_for_prospectivity_mapping_of_iron-oxide_apatite-bearing_mineralisation_in_Bergslagen_Sweden
• https://www.researchgate.net/publication/229347041_Predictive_mapping_of_prospectivity_and_quantitative_estimation_of_undiscovered_VMS_deposits_in_Skellefte_district_Sweden
• https://www.researchgate.net/publication/260086947_PRELIMINARY_GIS-BASED_ANALYSIS_OF_REGIONAL-SCALE_VMS_PROSPECTIVITY_IN_THE_SKELLEFTE_REGION_SWEDEN

Tanzânia

• https://www.sciencedirect.com/science/article/pii/S2666261224000270 -> Machine learning based prospect targeting: A case of gold occurrence in central parts of Tanzania, East Africa

Uganda

• https://www.researchgate.net/publication/242339962_Predictive_mapping_for_orogenic_gold_prospectivity_in_Uganda
• https://www.researchgate.net/publication/262566098_Predictive_Mapping_of_Prospectivity_for_Orogenic_Gold_in_Uganda
• https://www.researchgate.net/publication/381219015_Machine_Learning_Application_in_Predictive_Mineral_Mapping_of_Southwestern_Uganda_Leveraging_Airborne_Magnetic_Radiometric_and_Electromagnetic_Data

Reino Unido

• https://www.researchgate.net/publication/383580839_Improved_mineral_prospectivity_mapping_using_graph_neural_networks

EUA

• https://www.researchgate.net/publication/338663292_A_Predictive_Geospatial_Exploration_Model_for_Mississippi_Valley_Type_Pb-Zn_Mineralization_in_the_Southeast_Missouri_Lead_District
• https://www.sciencedirect.com/science/article/abs/pii/S0375674218300396?via%3Dihub -> Machine learning strategies for classification and prediction of alteration facies: Examples from the Rosemont Cu-Mo-Ag skarn deposit, SE Tucson Arizona
• [presentation of the above!] https://www.slideshare.net/JuanCarlosOrdezCalde/geology-chemostratigraphy-and-alteration-geochemistry-of-the-rosemont-cumoag-skarn-deposit-southern-arizona
• https://github.com/rohitash-chandra/research/blob/master/presentations/CSIRO%20Minerals-Seminar-September2022.pdf -> Machine Learning for Mineral Exploration: A Data Odyssey
  • Video https://www.youtube.com/watch?v=zhXuPQy7mk8&t=561s -> Talks about using plate subduction and associated statistics via GPlates

Zâmbia

• https://www.researchgate.net/publication/263542565_APPLICATION_OF_REMOTE_SENSING_AND_SPATIAL_DATA_INTEGRATION_TO_PREDICT_POTENTIAL_ZONES_FOR_AQUAMARINE-BEARING_PEGMATITES_LUNDAZI_AREA_NORTHEAST_ZAMBIA
• https://www.researchgate.net/publication/264041472_Geological_and_Mineral_Potential_Mapping_by_Geoscience_Data_Integration

Zimbábue

• https://www.researchgate.net/publication/260792212_Nickel_Sulphide_Deposits_in_Archaean_Greenstone_Belts_in_Zimbabwe_Review_and_Prospectivity_Analysis

GENERAL PAPERS

Visão geral

• https://www.sciencedirect.com/science/article/pii/S2772883824000347 -> A review on the applications of airborne geophysical and remote sensing datasets in epithermal gold mineralisation mapping
• https://www.researchgate.net/publication/353530416_A_Systematic_Review_on_the_Application_of_Machine_Learning_in_Exploiting_Mineralogical_Data_in_Mining_and_Mineral_Industry
• https://www.researchgate.net/publication/365777421_Computer_Vision_and_Pattern_Recognition_for_the_Analysis_of_2D3D_Remote_Sensing_Data_in_Geoscience_A_Survey - Computer Vision and Pattern Recognition for the Analysis of 2D/3D Remote Sensing Data in Geoscience: A Survey
• https://www.researchgate.net/publication/352104303_Deep_Learning_for_Geophysics_Current_and_Future_Trends
• https://www.proquest.com/openview/e7bec6c8ee50183b5049516b000d4f5c/1?pq-origsite=gscholar&cbl=18750&diss=y -> Probabilistic Knowledge-Guided Machine Learning in Engineering and Geoscience Systems
• KGMLPrescribedFires repository for one paper / part of above dissertation

Depósitos

• https://pubs.er.usgs.gov/publication/ofr20211049 -> Deposit Classification Scheme for the Critical Minerals Mapping Initiative Global Geochemical Database

ESG

• https://www.escubed.org/journals/earth-science-systems-and-society/articles/10.3389/esss.2024.10109/full -> Geospatial Data and Deep Learning Expose ESG Risks to Critical Raw Materials Supply: The Case of Lítio

Geoquímica

Causal Discovery and Deep Learning Algorithms for Detecting Geochemical Patterns Associated with Gold-Polymetallic Mineralization: A Case Study of the Edongnan Region

• https://link.springer.com/article/10.1007/s11053-024-10408-3 -> A New Sphalerite Thermometer Based on Machine Learning with Trace Element Geochemistry
• https://www.researchgate.net/publication/378150628_A_SMOTified_extreme_learning_machine_for_identifying_mineralization_anomalies_from_geochemical_exploration_data_a_case_study_from_the_Yeniugou_area_Xinjiang_China A SMOTified extreme learning machine for identifying mineralization anomalies from geochemical exploration data
• https://ui.adsabs.harvard.edu/abs/2018EGUGA..20.4169R/abstract -> Accelerating minerals exploration with in-field characterisation, sample tracking and active machine learning
• https://www.researchgate.net/publication/375509344_Alteration_assemblage_characterization_using_machine_learning_applied_to_high_resolution_drill-core_images_hyperspectral_data_and_geochemistry
• https://qspace.library.queensu.ca/items/38f52d19-609d-4916-bcd0-3ce20675dee3/full - > Application of Computational Methods to Data Integration and Geoscientific Problems in Mineral Exploration and Mining
• https://www.sciencedirect.com/science/article/pii/S0169136822005509?dgcid=rss_sd_all -> Applying neural networks-based modelling to the prediction of mineralization: A case-study using the Western Australian Geochemistry (WACHEM) database
• https://www.sciencedirect.com/science/article/pii/S0169136824002099 -> Development of a machine learning model to classify mineral deposits using sphalerite chemistry and mineral assemblages
• https://www.sciencedirect.com/science/article/pii/S0169136824002403 -> Discrimination of deposit types using magnetite geochemistry based on machine learning
• https://www.researchgate.net/publication/302595237_A_machine_learning_approach_to_geochemical_mapping
• https://www.researchgate.net/publication/369300132_DEEP-LEARNING_IDENTIFICATION_OF_ANOMALOUS_DATA_IN_GEOCHEMICAL_DATASETS_DEEP-LEARNING_IDENTIFICATION_OF_ANOMALOUS_DATA_IN_GEOCHEMICAL_DATASETS
• https://www.researchgate.net/publication/378549920_Denoising_of_geochemical_data_using_deep_learning-Implications_for_regional_surveys -> Denoising of Geochemical Data using Deep Learning–Implications for Regional Surveys]
• https://www.researchgate.net/publication/368489689_Discrimination_of_Pb-Zn_deposit_types_using_sphalerite_geochemistry_New_insights_from_machine_learning_algorithm
• https://www.researchgate.net/publication/381369176_Effectiveness_of_LOF_iForest_and_OCSVM_in_detecting_anomalies_in_stream_sediment_geochemical_data#:~:text=LOF%20outperformed%20iForest%20and%20OCSVM,patterns%20in%20the%20iForest%20map
• https://dzkjqb.cug.edu.cn/en/article/doi/10.19509/j.cnki.dzkq.tb20220423 -> Gaussian mixture model in geochemical anomaly delineation of stream sediments: A case study of Xupu, Hunan Province [UNSEEN ]
• https://www.sciencedirect.com/science/article/pii/S0883292724002427 -> Geologically constrained unsupervised dual-branch deep learning algorithm for geochemical anomalies identification
• https://www.researchgate.net/publication/365953549_Incorporating_the_genetic_and_firefly_optimization_algorithms_into_K-means_clustering_method_for_detection_of_porphyry_and_skarn_Cu-related_geochemical_footprints_in_Baft_district_Kerman_Iran
• https://www.researchgate.net/publication/369768936_Infomax-based_deep_autoencoder_network_for_recognition_of_multi-element_geochemical_anomalies_linked_to_mineralization -> Paywalled
• https://www.sciencedirect.com/science/article/abs/pii/S0098300424001626 -> Local phase-constrained convolutional autoencoder network for identifying multivariate geochemical anomalies
• https://www.researchgate.net/publication/354564681_Machine_Learning_for_Identification_of_Primary_Water_Concentrations_in_Mantle_Pyroxene
• https://www.researchgate.net/publication/366210211_Machine_Learning_Prediction_of_Ore_Deposit_Genetic_Type_Using_Magnetite_Geochemistry
• https://link.springer.com/article/10.1007/s42461-024-01013-2 -> NIR-Spectroscopy and Machine Learning Models to Pre-concentrate Copper Hosted Within Sedimentary Rocks[UNSEEN]
• https://www.researchsquare.com/article/rs-4106957/v1 -> Multi-element geochemical anomaly recognition applying geologically-constrained convolutional deep learning algorithm with Butterworth filtering
• https://www.researchgate.net/publication/369241349_Quantifying_continental_crust_thickness_using_the_machine_learning_method
• https://link.springer.com/article/10.1007/s11004-024-10158-1 -> Spatial-Spectrum Two-Branch Model Based on a Superpixel Graph Convolutional Network and 1DCNN for Geochemical Anomaly Identification
• https://www.researchgate.net/publication/334651800_Using_machine_learning_to_estimate_a_key_missing_geochemical_variable_in_mining_exploration_Application_of_the_Random_Forest_algorithm_to_multi-sensor_core_logging_data

Apatita

• https://www.researchgate.net/publication/377892369_Apatite_trace_element_composition_as_an_indicator_of_ore_deposit_types_A_machine_learning_approachApatite trace element composition as an indicator of ore deposit types: A machine learning approach
• https://www.researchgate.net/publication/369729999_Visual_Interpretation_of_Machine_Learning_Genetical_Classification_of_Apatite_from_Various_Ore_Sources

Geologia

Alteração

• https://ieeexplore.ieee.org/abstract/document/10544529 -> Remote sensing data processing using convolutional neural networks for mapping alteration zones [UNSEEN]

Profundidade

• https://www.researchgate.net/publication/332263305_A_speedy_update_on_machine_learning_applied_to_bedrock_mapping_using_geochemistry_or_geophysics_examples_from_the_Pacific_Rim_and_nearby
• https://eprints.utas.edu.au/32368/ - thesis paper update
• https://www.frontiersin.org/journals/earth-science/articles/10.3389/feart.2024.1407173/full -> Deep learning for geological mapping in the overburden area
• https://www.researchgate.net/publication/280038632_Estimating_the_fill_thickness_and_bedrock_topography_in_intermontane_valleys_using_artificial_neural_networks_-_Supporting_Information
• https://www.researchgate.net/publication/311783770_Mapping_the_global_depth_to_bedrock_for_land_surface_modeling
• https://www.researchgate.net/publication/379813337_Contribution_to_advancing_aquifer_geometric_mapping_using_machine_learning_and_deep_learning_techniques_a_case_study_of_the_AL_Haouz-Mejjate_aquifer_Marrakech_Morocco
• https://www.linkedin.com/pulse/depth-basement-modelling-machine-learning-perspective-n5gyc/?trackingId=qFSktvVPUiSa2V2nlmXVoQ%3D%3D

Drill Core

• https://pubmed.ncbi.nlm.nih.gov/35776744/ - Deep learning based lithology classification of drill core images
• https://www.researchgate.net/publication/381445417_Machine_Learning_for_Lithology_Analysis_using_a_Multi-Modal_Approach_of_Integrating_XRF_and_XCT_data
• https://www.researchgate.net/publication/379760986_A_machine_vision_approach_for_detecting_changes_in_drill_core_textures_using_optical_images
• https://www.sciencedirect.com/science/article/pii/S2949891024002112 -> Sensitivity analysis of similarity learning models for well-intervals based on logging data
• https://www.sciencedirect.com/science/article/pii/S2949891024003828 -> CoreViT: a new vision transformer model for lithology identification in cores

Em geral

• https://www.sciencedirect.com/science/article/pii/S0034425724002323 -> Deep learning-based geological map generation using geological routes
• https://www.researchgate.net/publication/354781583_Deep_learning_framework_for_geological_symbol_detection_on_geological_maps
• https://www.researchgate.net/publication/335104674_Does_shallow_geological_knowledge_help_neural-networks_to_predict_deep_units
• https://www.researchgate.net/publication/379939974_Graph_convolutional_network_for_lithological_classification_and_mapping_using_stream_sediment_geochemical_data_and_geophysical_data
• https://www.sciencedirect.com/science/article/abs/pii/S0098300424001493-> FlexLogNet: A flexible deep learning-based well-log completion method of adaptively using what you have to predict what you are missing
• https://ieeexplore.ieee.org/abstract/document/10493129 -> Geological Background Prototype Learning Enhanced Network for Remote Sensing-Based Engineering Geological Lithology Interpretation in Highly Vegetated Areas [Unseen]
• https://www.sciencedirect.com/science/article/pii/S2096249524000619 -> Generating extremely low-dimensional representation of subsurface earth models using vector quantization and deep Autoencoder
• https://www.researchgate.net/publication/370175012_GeoPDNN_A_Semisupervised_Deep_Learning_Neural_Network_Using_Pseudolabels_for_Three-dimensional_Urban_Geological_Modelling_and_Uncertainty_Analysis_from_Borehole_Data
• https://www.researchsquare.com/article/rs-4805227/v1 -> Synergizing AI with Geology: Exploring VisionTransformers for Rock Classification
• https://www.researchgate.net/publication/343511849_Identification_of_intrusive_lithologies_in_volcanic_terrains_in_British_Columbia_by_machine_learning_using_Random_Forests_the_value_of_using_a_soft_classifier
• https://www.sciencedirect.com/science/article/pii/S0169136824000921 -> Machine learning-based field geological mapping: A new exploration of geological survey data acquisition strategy https://www.researchgate.net/publication/324411647_Predicting_rock_type_and_detecting_hydrothermal_alteration_using_machine_learning_and_petrophysical_properties_of_the_Canadian_Malartic_ore_and_host_rocks_Pontiac_Subprovince_Quebec_Canada
• https://www.sciencedirect.com/science/article/abs/pii/S0895981124001743 -> Utilizing Random Forest algorithm for identifying mafic and ultramafic rocks in the Gameleira Suite, Archean-Paleoproterozoic basement of the Brasília Belt, Brazil
• https://arxiv.org/pdf/2407.18100 -> DINOv2 Rocks Geological Image Analysis: Classification,

Geochronology

• https://www.researchgate.net/publication/379077847_Tracing_Andean_Origins_A_Machine_Learning_Framework_for_Lead_Isotopes

Geomorphology

• https://agu.confex.com/agu/fm18/mediafile/Handout/Paper427843/Landforms%20Poster.pdf -> Using machine learning to classify landforms for minerals exploration
• https://www.tandfonline.com/doi/abs/10.1080/13658816.2024.2414409 -> GeomorPM: a geomorphic pretrained model integrating convolution and Transformer architectures based on DEM data

Lithology

• https://link.springer.com/article/10.1007/s11053-024-10396-4 -> Interpretable SHAP Model Combining Meta-learning and Vision Transformer for Lithology Classification Using Limited and Unbalanced Drilling Data in Well Logging [UNSEN]
• https://www.nature.com/articles/s41598-024-66199-3 -> Machine learning and remote sensing-based lithological mapping of the Duwi Shear-Belt area, Central Eastern Desert, Egypt
• https://link.springer.com/article/10.1007/s11053-024-10375-9 - SsL-VGMM: A Semisupervised Machine Learning Model of Multisource Data Fusion for Lithology Prediction [UNSEEN]
• https://www.researchgate.net/publication/380719080_An_integrated_machine_learning_framework_with_uncertainty_quantification_for_three-dimensional_lithological_modeling_from_multi-source_geophysical_data_and_drilling_data
• https://www.bio-conferences.org/articles/bioconf/pdf/2024/34/bioconf_rena23_01005.pdf -> Lithological Mapping using Artificial Intelligence and Remote Sensing data: A Case Study of Bab Boudir region Morocco

Mineralogia

• https://pubs.geoscienceworld.org/msa/ammin/article-abstract/doi/10.2138/am-2023-9092/636861/The-application-of-transfer-learning-in-optical -> The application of “transfer learning” in optical microscopy: the petrographic classification of metallic minerals
• https://www.researchgate.net/publication/385074584_Deep_Learning-Based_Mineral_Classification_Using_Pre-Trained_VGG16_Model_with_Data_Augmentation_Challenges_and_Future_Directions

Estratigrafia

• https://www.researchgate.net/publication/335486001_A_Stratigraphic_Prediction_Method_Based_on_Machine_Learning
• https://www.researchgate.net/publication/346641320_Classifying_basin-scale_stratigraphic_geometries_from_subsurface_formation_tops_with_machine_learning

Estrutura

• https://www.sciencedirect.com/science/article/pii/S0098300421000285 -> A machine learning model for structural trend fields
• https://onlinelibrary.wiley.com/doi/full/10.1111/1365-2478.13589 -> Inferring fault structures and overburden depth in 3D from geophysical data using machine learning algorithms – A case study on the Fenelon gold deposit, Quebec, Canada
• https://www.sciencedirect.com/science/article/pii/S019181412400138X -> Mapping paleostress trajectories by means of the clustering of reduced stress tensors determined from homogeneous and heterogeneous data sets
• https://www.researchgate.net/publication/332267249_Seismic_fault_detection_using_an_encoder-decoder_convolutional_neural_network_with_a_small_training_set
• https://www.researchgate.net/publication/377168034_Unsupervised_machine_learning_and_depth_clusters_of_Euler_deconvolution_of_magnetic_data_a_new_approach_to_imaging_geological_structures
• https://academic.oup.com/gji/advance-article/doi/10.1093/gji/ggae226/7701418 -> Use of Decision Tree Ensembles for Crustal Structure Imaging from Receiver Functions

Tectonics

• https://www.researchgate.net/publication/371594975_Assessing_plate_reconstruction_models_using_plate_driving_force_consistency_tests
• https://www.researchgate.net/publication/333182666_Decoding_Earth's_plate_tectonic_history_using_sparse_geochemical_data
• https://www.researchgate.net/publication/376519740_Machine_learning_and_tectonic_setting_determination_Bridging_the_gap_between_Earth_scientists_and_data_scientists
• https://pubs.geoscienceworld.org/gsa/geology/article-abstract/doi/10.1130/G52466.1/648458/Prediction-of-CO2-content-in-mid-ocean-ridge -> Prediction of CO2 content in mid-ocean ridge basalts via a machine learning approach

Geofísica

Fundação

• https://www.researchgate.net/publication/373714604_Seismic_Foundation_Model_SFM_a_new_generation_deep_learning_model_in_geophysics

Em geral

• https://essopenarchive.org/users/841077/articles/1231187-bayesian-inference-in-geophysics-with-ai-enhanced-markov-chain-monte-carlo -> Bayesian Inference in Geophysics with AI-enhanced Markov chain Monte Carlo
• https://www.researchgate.net/publication/353789276_Geology_differentiation_by_applying_unsupervised_machine_learning_to_multiple_independent_geophysical_inversions
• https://www.sciencedirect.com/science/article/pii/S001379522100137X - Joint interpretation of geophysical data: Applying machine learning to the modeling of an evaporitic sequence in Villar de Cañas (Spain)
• https://www.sciencedirect.com/science/article/pii/S2666544121000253 - Microleveling aerogeophysical data using deep convolutional network and MoG-RPCA
• https://www.researchgate.net/publication/368550674_Objective_classification_of_high-resolution_geophysical_data_Empowering_the_next_generation_of_mineral_exploration_in_Sierra_Leone
• https://datarock.com.au/blog/transfer-learning-with-seismic-attributes -> Transfer Learning with Seismic Attributes

Potential Fields

• https://api.research-repository.uwa.edu.au/ws/portalfiles/portal/390212334/THESIS_-_DOCTOR_OF_PHILOSOPHY_-_SMITH_Luke_Thomas_-_2023_.pdf -> Potential Field Geophysics Enhancement Using Conteporary Deep Learning

EM

• https://d197for5662m48.cloudfront.net/documents/publicationstatus/206704/preprint_pdf/59681a0a2c571bc2a9006f37517bc6ef.pdf -> A Fast Three-dimensional Imaging Scheme of Airborne Time Domain Electromagnetic Data using Deep Learning
• https://www.researchgate.net/publication/351507441_A_Neural_Network-Based_Hybrid_Framework_for_Least-Squares_Inversion_of_Transient_Electromagnetic_Data
• https://www.researchgate.net/profile/Yunhe-Liu/publication/382196526_An_Efficient_Bayesian_Inference_for_Geo-electromagnetic_Data_Inversion_based_on_Surrogate_Modeling_with_Adaptive_Sampling_DNN
• https://www.researchgate.net/publication/325980016_Agglomerative_hierarchical_clustering_of_airborne_electromagnetic_data_for_multi-scale_geological_studies
• https://www.earthdoc.org/content/papers/10.3997/2214-4609.202410980 -> Deep Learning Assisted 2-D Current Density Modelling of Very Low Frequency Electromagnetic Data
• https://npg.copernicus.org/articles/26/13/2019/ -> Denoising stacked autoencoders for transient electromagnetic signal denoising
• https://www.researchgate.net/publication/373836226_An_information_theoretic_Bayesian_uncertainty_analysis_of_AEM_systems_over_Menindee_Lake_Australia -> An information theoretic Bayesian uncertainty analysis of AEM systems over Menindee Lake, Australia
• https://www.researchgate.net/publication/348850484_Effect_of_Data_Normalization_on_Neural_Networks_for_the_Forward_Modelling_of_Transient_Electromagnetic_Data
• https://www.researchgate.net/publication/342153377_Fast_imaging_of_time-domain_airborne_EM_data_using_deep_learning_technology
• https://library.seg.org/doi/10.4133/JEEG4.2.93 -> Neural Network Interpretation of High Frequency Electromagnetic Ellipticity Data Part I: Understanding the Half-Space and Layered Earth Response
• https://arxiv.org/abs/2207.12607 -> Physics Embedded Machine Learning for Electromagnetic Data Imaging
• https://academic.oup.com/gji/advance-article/doi/10.1093/gji/ggae244/7713480 -> Physics-guided deep learning-based inversion for airborne electromagnetic data
• https://library.seg.org/doi/abs/10.1190/geo2024-0282.1 -> Comparative Analysis of Deep Learning and Traditional Airborne Electromagnetic Data Processing: A Case Study [UNSEEN]
• https://www.researchgate.net/publication/359441000_Surface_parameters_and_bedrock_properties_covary_across_a_mountainous_watershed_Insights_from_machine_learning_and_geophysics
• https://www.researchgate.net/publication/337166479_Using_machine_learning_to_interpret_3D_airborne_electromagnetic_inversions
• https://www.researchgate.net/publication/344397798_TEMDnet_A_Novel_Deep_Denoising_Network_for_Transient_Electromagnetic_Signal_With_Signal-to-Image_Transformation
• https://www.researchgate.net/publication/366391168_Two-dimensional_fast_imaging_of_airborne_EM_data_based_on_U-net

ERT

• https://www.sciencedirect.com/science/article/pii/S0013795224001893 -> Geo-constrained clustering of resistivity data revealing the heterogeneous lithological architectures and the distinctive geoelectrical signature of shallow deposits

Gravidade

• https://ieeexplore.ieee.org/abstract/document/10597585 -> 3D Basement Relief and Density Inversion Based on EfficientNetV2 Deep Learning Network [UNSEEN]
• https://link.springer.com/article/10.1007/s11770-024-1096-5 -> 3D gravity inversion using cycle-consistent generative adversarial network [UNSEEN]
• https://www.researchgate.net/publication/365142017_3D_gravity_inversion_based_on_deep_learning
• https://www.researchgate.net/publication/378930477_A_Deep_Learning_Gravity_Inversion_Method_Based_on_a_Self-Constrained_Network_and_Its_Application
• https://www.researchgate.net/publication/362276214_DecNet_Decomposition_network_for_3D_gravity_inversion -> Olympic Dam example here
• https://www.researchgate.net/publication/368448190_Deep_Learning_to_estimate_the_basement_depth_by_gravity_data_using_Feedforward_neural_network
• https://www.researchgate.net/publication/326231731_Depth_and_Lineament_Maps_Derived_from_North_Cameroon_Gravity_Data_Computed_by_Artificial_Neural_Network_International_Journal_of_Geophysics_vol_2018_Article_ID_1298087_13_pages_2018
• https://www.researchgate.net/publication/366922016_Fast_imaging_for_the_3D_density_structures_by_machine_learning_approach
• https://www.researchgate.net/publication/370230217_Inversion_of_the_Gravity_Gradiometry_Data_by_ResUet_Network_An_Application_in_Nordkapp_Basin_Barents_Sea
• https://www.frontiersin.org/journals/earth-science/articles/10.3389/feart.2022.897055/full -> High-precision downward continuation of the potential field based on the D-Unet network
• https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10672527 -> RTM Gravity Forward Modeling Using Improved Fully Connected Deep Neural Networks

Hyperspectral

• https://www.researchgate.net/publication/380391736_A_review_on_hyperspectral_imagery_application_for_lithological_mapping_and_mineral_prospecting_Machine_learning_techniques_and_future_prospects
• https://www.researchgate.net/publication/372876863_Ore-Grade_Estimation_from_Hyperspectral_Data_Using_Convolutional_Neural_Networks_A_Case_Study_at_the_Olympic_Dam_Iron_Oxide_Copper-Gold_Deposit_Australia [UNSEEN]

Joint Inversion

• https://www.researchgate.net/publication/383454185_Deep_joint_inversion_of_electromagnetic_seismic_and_gravity_data
• https://ieeexplore.ieee.org/abstract/document/10677418 -> Joint Inversion of Seismic and Resistivity Data Powered by Deep-learning [UNSEEN]

Magnetics

• https://www.researchgate.net/publication/348697645_3D_geological_structure_inversion_from_Noddy-generated_magnetic_data_using_deep_learning_methods
• https://www.researchgate.net/publication/360288249_3D_Inversion_of_Magnetic_Gradient_Tensor_Data_Based_on_Convolutional_Neural_Networks
• https://www.researchgate.net/publication/295902270_Artificial_neural_network_inversion_of_magnetic_anomalies_caused_by_2D_fault_structures
• https://www.researchgate.net/publication/354002966_Convolutional_neural_networks_for_the_characterization_of_magnetic_anomalies
• https://www.researchgate.net/publication/354772176_Convolution_Neural_Networks_Applied_to_the_Interpretation_of_Lineaments_in_Aeromagnetic_Data
• https://www.researchgate.net/publication/363550362_High-precision_downward_continuation_of_the_potential_field_based_on_the_D-Unet_network
• https://www.sciencedirect.com/science/article/pii/S0169136822004279?via%3Dihub -> Magnetic grid resolution enhancement using machine learning: A case study from the Eastern Goldfields Superterrane
• https://www.researchgate.net/publication/347173621_Predicting_Magnetization_Directions_Using_Convolutional_Neural_Networks -> Paywalled
• https://www.researchgate.net/publication/361114986_Reseaux_de_Neurones_Convolutifs_pour_la_Caracterisation_d'Anomalies_Magnetiques -> French original of the above

Magnetotellurics

• https://advancesincontinuousanddiscretemodels.springeropen.com/articles/10.1186/s13662-024-03842-3 -> 2D magnetotelluric imaging method based on visionary self-attention mechanism and data science
• https://ieeexplore.ieee.org/abstract/document/10530937 -> A Magnetotelluric Data Denoising Method Based on Lightweight Ensemble Learning [UNSEEN]
• https://academic.oup.com/gji/advance-article/doi/10.1093/gji/ggae166/7674890 -> Deep basin conductor characterization using machine learning-assisted magnetotelluric Bayesian inversion in the SW Barents Sea
• http://en.dzkx.org/article/doi/10.6038/cjg2024R0580 -> Fast inversion method of apparent resistivity based on deep learning
• https://www.researchgate.net/publication/367504269_Flexible_and_accurate_prior_model_construction_based_on_deep_learning_for_2D_magnetotelluric_data_inversion
• https://www.sciencedirect.com/science/article/pii/S2214579624000510 -> Intelligent Geological Interpretation of AMT Data Based on Machine Learning
• https://ieeexplore.ieee.org/abstract/document/10551853 -> Magnetotelluric Data Inversion Based on Deep Learning with the Self-attention Mechanism
• https://www.researchgate.net/publication/361741409_Physics-Driven_Deep_Learning_Inversion_with_Application_to_Magnetotelluric
• https://www.researchgate.net/publication/355568465_Stochastic_inversion_of_magnetotelluric_data_using_deep_reinforcement_learning
• https://www.researchgate.net/publication/354360079_Two-dimensional_deep_learning_inversion_of_magnetotelluric_sounding_data
• https://ieeexplore.ieee.org/abstract/document/10530923 -> Three Dimensional Magnetotelluric Forward Modeling Through Deep Learning

Passive Seismic

• https://nature.com/articles/s41467-020-17841-x -> Clustering earthquake signals and background noises in continuous seismic data with unsupervised deep learning
• https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2022GL099053 -> Eikonal Tomography With Physics-Informed Neural Networks: Rayleigh Wave Phase Velocity in the Northeastern Margin of the Tibetan Plateau
• https://arxiv.org/abs/2403.15095 -> End-to-End Mineral Exploration with Artificial Intelligence and Ambient Noise Tomography
• https://www.nature.com/articles/s41598-019-50381-z -> High-resolution seismic tomography of Long Beach, CA using machine learning

Seismic

• https://www.sciencedirect.com/science/article/pii/S0040195124002166 -> Reprocessing and interpretation of legacy seismic data using machine learning from the Granada Basin, Spain
• https://ojs.uni-miskolc.hu/index.php/geosciences/article/view/3313 -> EDGE DETECTION OF TOMOGRAPHIC IMAGES USING TRADITIONAL AND DEEP LEARNING TOOLS

Surface Resistivity

• https://www.researchgate.net/publication/367606119_Deriving_Surface_Resistivity_from_Polarimetric_SAR_Data_Using_Dual-Input_UNet

Incerteza

• https://library.seg.org/doi/abs/10.1190/GEM2024-084.1 -> Quantifying uncertainty in 3D geophysical inverse problems: Advancing from deterministic to Bayesian and deep generative models [UNSEEN]

Geothermal

• https://www.osti.gov/biblio/2335471 - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada [adjacent but interesting]
• https://gdr.openei.org/submissions/1402 - Associated code
• https://catalog.data.gov/dataset/python-codebase-and-jupyter-notebooks-applications-of-machine-learning-techniques-to-geoth
• https://www.researchgate.net/publication/341418586_Preliminary_Report_on_Applications_of_Machine_Learning_Techniques_to_the_Nevada_Geothermal_Play_Fairway_Analysis

Mapas

• https://www.researchgate.net/publication/347786302_Semantic_Segmentation_Deep_Learning_for_Extracting_Surface_Mine_Extents_from_Historic_Topographic_Maps

Mineral

• https://www.researchgate.net/publication/357942198_Mineral_classification_of_lithium-bearing_pegmatites_based_on_laser-induced_breakdown_spectroscopy_Application_of_semi-supervised_learning_to_detect_known_minerals_and_unknown_material
• https://iopscience.iop.org/article/10.1088/1755-1315/1032/1/012046 -> Classifying Minerals using Deep Learning Algorithms
• https://www.researchgate.net/publication/370835450_Predicting_new_mineral_occurrences_and_planetary_analog_environments_via_mineral_association_analysis
• https://www.researchgate.net/publication/361230503_What_is_Mineral_Informatics

PNL

• https://www.researchgate.net/publication/358616133_Chinese_Named_Entity_Recognition_in_the_Geoscience_Domain_Based_on_BERT
• https://www.researchgate.net/publication/339394395_Dictionary-Based_Automated_Information_Extraction_From_Geological_Documents_Using_a_Deep_Learning_Algorithm
• https://www.aclweb.org/anthology/2020.lrec-1.568/ -> Embeddings for Named Entity Recognition in Geoscience Portuguese Literature
• https://www.researchgate.net/publication/359186219_Few-shot_learning_for_name_entity_recognition_in_geological_text_based_on_GeoBERT
• https://www.researchgate.net/publication/333464862_GeoDocA_-_Fast_Analysis_of_Geological_Content_in_Mineral_Exploration_Reports_A_Text_Mining_Approach
• https://www.researchgate.net/publication/366710921_Geological_profile-text_information_association_model_of_mineral_exploration_reports_for_fast_analysis_of_geological_content
• https://www.researchgate.net/publication/330835955_Geoscience_Keyphrase_Extraction_Algorithm_Using_Enhanced_Word_Embedding [UNSEEN]
• https://www.researchgate.net/publication/332997161_GNER_A_Generative_Model_for_Geological_Named_Entity_Recognition_Without_Labeled_Data_Using_Deep_Learning
• https://www.researchgate.net/publication/321850315_Information_extraction_and_knowledge_graph_construction_from_geoscience_literature
• https://www.researchgate.net/publication/365929623_Named_Entity_Annotation_Schema_for_Geological_Literature_Mining_in_the_Domain_of_Porphyry_Copper_Deposits
• https://www.researchgate.net/publication/329621358_Ontology-Based_Enhanced_Word_Embedding_for_Automated_Information_Extraction_from_Geoscience_Reports
• https://www.researchgate.net/publication/379808469_Ontology-driven_relational_data_mapping_for_constructing_a_knowledge_graph_of_porphyry_copper_deposits -> Ontology-driven relational data mapping for constructing a knowledge graph of porphyry copper deposits
• https://www.researchgate.net/publication/327709479_Prospecting_Information_Extraction_by_Text_Mining_Based_on_Convolutional_Neural_Networks-A_Case_Study_of_the_Lala_Copper_Deposit_China
• https://ieeexplore.ieee.org/document/8711400 -> Research and Application on Geoscience Literature Knowledge Discovery Technology
• https://www.researchgate.net/publication/332328315_Text_Mining_to_Facilitate_Domain_Knowledge_Discovery
• https://www.researchgate.net/publication/351238658_Understanding_Ore-Forming_Conditions_using_Machine_Reading_of_Text
• https://www.researchgate.net/publication/359089763_Visual_analytics_and_information_extraction_of_geological_content_for_text-based_mineral_exploration_reports
• https://www.researchgate.net/publication/354754114_What_is_this_article_about_Generative_summarization_with_the_BERT_model_in_the_geosciences_domain
• https://www.slideshare.net/phcleverley/where-text-analytics-meets-geoscience -> Where text analytics meets geoscience

Petrografia

• https://www.researchgate.net/publication/335226326_Digital_petrography_Mineralogy_and_porosity_identification_using_machine_learning_algorithms_in_petrographic_thin_section_images

Last edited: 29/09/2020 The below are a collection of works from when I was doing a review

Public Mineral Prospectivity Mapping

Visão geral

• https://www.researchgate.net/publication/331852267_Applying_Spatial_Prospectivity_Mapping_to_Exploration_Targeting_Fundamental_Practical_issues_and_Suggested_Solutions_for_the_Future
• https://www.researchgate.net/publication/284890591_Geochemical_Anomaly_and_Mineral_Prospectivity_Mapping_in_GIS
• https://www.researchgate.net/publication/341472154_Geodata_Science-Based_Mineral_Prospectivity_Mapping_A_Review
• https://www.researchgate.net/publication/275338029_Introduction_to_the_Special_Issue_GIS-based_mineral_potential_modelling_and_geological_data_analyses_for_mineral_exploration
• https://www.researchgate.net/publication/339074334_Introduction_to_the_special_issue_on_spatial_modelling_and_analysis_of_ore-forming_processes_in_mineral_exploration_targeting
• https://www.researchgate.net/publication/317319129_Natural_Resources_Research_Publications_on_Geochemical_Anomaly_and_Mineral_Potential_Mapping_and_Introduction_to_the_Special_Issue_of_Papers_in_These_Fields
• https://www.researchgate.net/publication/46696293_Selection_of_coherent_deposit-type_locations_and_their_application_in_data-driven_mineral_prospectivity_mapping

Geoquímica

• https://www.researchgate.net/publication/375926319_A_paradigm_shift_in_Precambrian_research_driven_by_big_data

• https://www.researchgate.net/publication/359447201_A_review_of_machine_learning_in_geochemistry_and_cosmochemistry_Method_improvements_and_applications

  • https://jaywen.com/files/He_2022_Applied_Geochemistry.pdf

• https://www.researchgate.net/publication/220164381_Application_of_geochemical_zonality_coefficients_in_mineral_prospectivity_mapping

• https://www.researchgate.net/publication/238505045_Analysis_and_mapping_of_geochemical_anomalies_using_logratio-transformed_stream_sediment_data_with_censored_values

• https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2022EA002626 -> Comparative Study on Three Autoencoder-Based Deep Learning Algorithms for Geochemical Anomaly Identification

• https://www.researchgate.net/publication/373758047_Decision-making_within_geochemical_exploration_data_based_on_spatial_uncertainty_-A_new_insight_and_a_futuristic_review

• https://www.researchgate.net/publication/331505001_Deep_learning_and_its_application_in_geochemical_mapping

• https://www.researchgate.net/publication/380262759_Factor_analysis_in_residual_soils_of_the_Iberian_Pyrite_Belt_Spain_Comparison_between_raw_data_log_transformation_data_and_compositional_data [UNSEEN]

• https://www.researchgate.net/publication/272091723_Geochemical_characteristics_of_mineral_deposits_Implications_for_ore_genesis

• https://www.researchgate.net/publication/257189047_Geochemical_mineralization_probability_index_GMPI_A_new_approach_to_generate_enhanced_stream_sediment_geochemical_evidential_map_for_increasing_probability_of_success_in_mineral_potential_mapping

• https://www.researchgate.net/publication/333497470_Integration_of_auto-encoder_network_with_density-based_spatial_clustering_for_geochemical_anomaly_detection_for_mineral_exploration

• https://www.researchgate.net/publication/319303831_Introduction_to_the_thematic_issue_Analysis_of_exploration_geochemical_data_for_mapping_of_anomalies

• https://www.researchgate.net/publication/356722687_Machine_learning-based_prediction_of_trace_element_concentrations_using_data_from_the_Karoo_large_igneous_province_and_its_application_in_prospectivity_mapping#fullTextFileContent

• https://www.degruyter.com/document/doi/10.2138/am-2023-9115/html -> Machine learning applied to apatite compositions for determining mineralization potential [UNSEEN]

• https://www.researchgate.net/publication/257026525_Primary_geochemical_characteristics_of_mineral_deposits_-_Implications_for_exploration

• https://www.researchgate.net/publication/283554338_Recognition_of_geochemical_anomalies_using_a_deep_autoencoder_network

  • https://zarmesh.com/wp-content/uploads/2017/04/Recognition-of-geochemical-anomalies-using-a-deep-autoencoder-network.pdf

• https://www.researchgate.net/publication/349606557_Robust_Feature_Extraction_for_Geochemical_Anomaly_Recognition_Using_a_Stacked_Convolutional_Denoising_Autoencoder [UNSEEN]

• https://www.researchgate.net/publication/375911531_Spatial_Interpolation_Using_Machine_Learning_From_Patterns_and_Regularities_to_Block_Models#fullTextFileContent

• https://www.researchgate.net/publication/259716832_Supervised_and_unsupervised_classification_of_near-mine_soil_Geochemistry_and_Geophysics_data

• https://www.researchgate.net/publication/277813662_Supervised_Geochemical_Anomaly_Detection_by_Pattern_Recognition

• https://www.researchgate.net/publication/249544991_Usefulness_of_stream_order_to_detect_stream_sediment_geochemical_anomalies

• https://www.researchgate.net/publication/321275541_Weighting_stream_sediment_geochemical_samples_as_exploration_indicator_of_deposit_-_type

Difuso

• https://www.researchgate.net/publication/272170968_A_Comparative_Analysis_of_Weights_of_Evidence_Evidential_Belief_Functions_and_Fuzzy_Logic_for_Mineral_Potential_Mapping_Using_Incomplete_Data_at_the_Scale_of_Investigation
• https://www.researchgate.net/publication/267816279_Fuzzification_of_continuous-value_spatial_evidence_for_mineral_prospectivity_mapping
• https://www.researchgate.net/publication/301635716_Union_score_and_fuzzy_logic_mineral_prospectivity_mapping_using_discretized_and_continuous_spatial_evidence_values

Incerteza

• https://deliverypdf.ssrn.com/delivery.php?ID=555064031119110002088087068121000096050036019060022069010050000053011056029076002067121000064004002088113115000107115017083105004026015092089005123065040099024112018026013043065104094012124120126039100033055018066074125089104115090100009064122122019003015085069021024027072126106082092110&EXT=pdf&INDEX=TRUE -> Estimating uncertainties in 3-D models of complex fold-and-thrust 2 belts: a case study of the Eastern Alps triangle zone
• https://www.researchgate.net/publication/333339659_Incorporating_conceptual_and_interpretation_uncertainty_to_mineral_prospectivity_modelling
• https://www.researchgate.net/publication/235443307_Managing_uncertainty_in_exploration_targeting
• https://www.researchgate.net/publication/255909185_The_upside_of_uncertainty_Identification_of_lithology_contact_zones_from_airborne_geophysics_and_satellite_data_using_random_forests_and_support_vector_machines

Geospatial Maps

Austrália

• https://www.researchgate.net/publication/334440382_Mapping_iron_oxide_Cu-Au_IOCG_mineral_potential_in_Australia_using_a_knowledge-driven_mineral_systems-based_approach

South Australia

• https://www.researchgate.net/publication/335313790_Prospectivity_modelling_of_the_Olympic_Cu-Au_Province - https://services.sarig.sa.gov.au/raster/ProspectivityModelling/wms?service=wms&version=1.1.1&REQUEST=GetCapabilities
• An assessment of the uranium and geothermal prospectivity of east-central South Australia - https://d28rz98at9flks.cloudfront.net/72666/Rec2011_034.pdf

NT

• https://www.researchgate.net/publication/285235798_An_assessment_of_the_uranium_and_geothermal_prospectivity_of_the_southern_Northern_Territory

WA

• https://www.researchgate.net/publication/273073675_Building_a_machine_learning_classifier_for_iron_ore_prospectivity_in_the_Yilgarn_Craton
• http://dmpbookshop.eruditetechnologies.com.au/product/district-scale-targeting-for-gold-in-the-yilgarn-craton-part-2-of-the-yilgarn-gold-exploration-targeting-atlas.do$55 purchase
• http://dmpbookshop.eruditetechnologies.com.au/product/mineral-prospectivity-of-the-king-leopold-orogen-and-lennard-shelf-analysis-of-potential-field-data-in-the-west-kimberley-region-geographical-product-n14bnzp.do
• http://dmpbookshop.eruditetechnologies.com.au/product/mineral-systems-analysis-of-the-west-musgrave-province-regional-structure-and-prospectivity-modelling-geographical-product-n12dzp.do
• http://dmpbookshop.eruditetechnologies.com.au/product/mineral-systems-analysis-of-the-west-musgrave-province-regional-structure-and-prospectivity-modelling.do $22 purchase
• https://researchdata.edu.au/predictive-mineral-discovery-gold-mineral/1209568?source=suggested_datasets - Predictive mineral discovery in the eastern Yilgarn Craton: an example of district-scale targeting of an orogenic gold mineral system - https://d28rz98at9flks.cloudfront.net/82617/Y4_Gold_Targeting.zip
• http://dmpbookshop.eruditetechnologies.com.au/product/prospectivity-analysis-of-the-halls-creek-orogen-western-australia-using-a-mineral-systems-approach-geographical-product-n15af3zp.do
• https://researchdata.edu.au/prospectivity-analysis-using-063-m436/1424743 - Prospectivity analysis using a mineral systems approach - Capricorn case study project CSIRO Prospectivity analysis using a mineral systems approach - Capricorn case study project (13.5 GB Download)
• http://dmpbookshop.eruditetechnologies.com.au/product/regional-scale-targeting-for-gold-in-the-yilgarn-craton-part-1-of-the-yilgarn-gold-exploration-targeting-atlas.do $55 purchase
• https://www.researchgate.net/publication/263928515_Towards_Australian_metallogenic_maps_through_space_and_time
• https://www.sciencedirect.com/science/article/abs/pii/S0301926810002111 - Yilgarn

Brasil

• https://www.researchgate.net/publication/340633563_CATALOG_OF_PROSPECTIVITY_MAPS_OF_SELECTED_AREAS_FROM_BRAZIL
• https://www.researchgate.net/publication/341936771_Modeling_of_Cu-Au_Prospectivity_in_the_Carajas_mineral_province_Brazil_through_Machine_Learning_Dealing_with_Imbalanced_Training_Data
• https://www.researchgate.net/publication/287270273_Nickel_prospective_modelling_using_fuzzy_logic_on_nova_Brasilandia_metasedimentary_belt_Rondonia_Brazil
• https://www.scielo.br/scielo.php?script=sci_arttext&pid=S2317-48892016000200261 - Sao Francisco Craton Nickel

Austrália

• https://www.researchgate.net/publication/248211737_A_continent-wide_study_of_Australia's_uranium_potential
• https://www.researchgate.net/publication/334440382_Mapping_iron_oxide_Cu-Au_IOCG_mineral_potential_in_Australia_using_a_knowledge-driven_mineral_systems-based_approach
• https://researchdata.edu.au/predictive-model-opal-mining-approach/673159/?refer_q=rows=15/sort=score%20desc/class=collection/p=2/q=mineral%20prospectivity%20map/ - Opal

SA

• https://data.gov.au/dataset/ds-ga-a8619169-1c2a-6697-e044-00144fdd4fa6/details?q= -> An assessment of the uranium and geothermal prospectivity of east central South Australia
• https://d28rz98at9flks.cloudfront.net/72666/Rec2011_034.pdf -> An assessment of the uranium and geothermal prospectivity of east-central South Australia
• https://www.pir.sa.gov.au/__data/assets/pdf_file/0011/239636/204581-001_wise_high.pdf - Eastern Gawler - WPA
• http://www.energymining.sa.gov.au/minerals/knowledge_centre/mesa_journal/previous_feature_articles/new_prospectivity_map
• https://catalog.sarig.sa.gov.au/geonetwork/srv/eng/catalog.search#/metadata/e59cd4ba-1a0a-4911-9e6a-58d80576678d - Olympic Domain IOCG Prospectivity model
• https://www.researchgate.net/publication/335313790_Prospectivity_modelling_of_the_Olympic_Cu-Au_Province - https://services.sarig.sa.gov.au/raster/ProspectivityModelling/wms?service=wms&version=1.1.1&REQUEST=GetCapabilities

WA

• https://www.sciencedirect.com/science/article/abs/pii/S0301926810002111 - Yilgarn Karol Czarnota
• https://www.researchgate.net/publication/229333177_Prospectivity_analysis_of_the_Plutonic_Marymia_Greenstone_Belt_Western_Australia
• https://www.researchgate.net/publication/280039091_Mineral_systems_approach_applied_to_GIS-based_2D-prospectivity_modelling_of_geological_regions_Insights_from_Western_Australia
• https://www.researchgate.net/publication/351238658_Understanding_Ore-Forming_Conditions_using_Machine_Reading_of_Text

NT

• https://www.researchgate.net/publication/285235798_An_assessment_of_the_uranium_and_geothermal_prospectivity_of_the_southern_Northern_Territory
• https://www.researchgate.net/publication/342352173_Modelling_gold_potential_in_the_Granites-Tanami_Orogen_NT_Australia_A_comparative_study_using_continuous_and_data-driven_techniques

Novo estado do estado

• https://www.resourcesandgeoscience.nsw.gov.au/miners-and-explorers/geoscience-information/projects/mineral-potential-mapping#_southern-_new-_england-_orogen-mineral-potential
• https://www.smedg.org.au/GSNSW_2019_Blevin.pdf - Eastern Lachlan Orogen
• https://www.researchgate.net/publication/265915602_Comparing_prospectivity_modelling_results_and_past_exploration_data_A_case_study_of_porphyry_Cu-Au_mineral_systems_in_the_Macquarie_Arc_Lachlan_Fold_Belt_New_South_Wales

Brasil

• https://www.researchgate.net/publication/340633563_CATALOG_OF_PROSPECTIVITY_MAPS_OF_SELECTED_AREAS_FROM_BRAZIL
• https://www.researchgate.net/publication/340633739_MINERAL_POTENTIAL_AND_OPORTUNITIES_FOR_THE_EXPLORATION_OF_NEW_GEOLOGICAL_GROUNDS_IN_BRAZIL
• https://www.semanticscholar.org/paper/Mineral-Potential-Mapping-for-Orogenic-Gold-in-the-Silva-Silva/a23a9ce4da48863da876758afa9e1d2723088853
• https://www.scielo.br/scielo.php?script=sci_arttext&pid=S2317-48892016000200261 - Supergene nickel deposits in outhwestern Sao Francisco Carton, Brazil

Carajas

• https://www.researchgate.net/publication/258466504_Self-Organizing_Maps_A_Data_Mining_Tool_for_the_Analysis_of_Airborne_Geophysical_Data_Collected_over_the_Brazilian_Amazon
• https://www.researchgate.net/publication/258647519_Semiautomated_geologic_mapping_using_self-organizing_maps_and_airborne_geophysics_in_the_Brazilian_Amazon
• https://www.researchgate.net/publication/235443304_GIS-Based_prospectivity_mapping_for_orogenic_gold_A_case_study_from_the_Andorinhas_region_Brasil
• https://www.researchgate.net/publication/341936771_Modeling_of_Cu-Au_Prospectivity_in_the_Carajas_mineral_province_Brazil_through_Machine_Learning_Dealing_with_Imbalanced_Training_Data
• https://www.researchgate.net/publication/332031621_Predictive_lithological_mapping_through_machine_learning_methods_a_case_study_in_the_Cinzento_Lineament_Carajas_Province_Brazil
• https://www.researchgate.net/publication/340633659_Copper-gold_favorability_in_the_Cinzento_Shear_Zone_Carajas_Mineral_Province
• https://www.researchgate.net/publication/329477409_Favorability_potential_for_IOCG_type_deposits_in_the_Riacho_do_Pontal_Belt_New_insights_for_identifying_prospects_of_IOCG-type_deposits_in_NE_Brazil
• https://www.researchgate.net/publication/339453836_Uranium_anomalies_detection_through_Random_Forest_regression
• https://d1wqtxts1xzle7.cloudfront.net/48145419/Artificial_neural_networks_applied_to_mi20160818-5365-odv4na.pdf?1471522188=&response-content-disposition=inline%3B+filename%3DArtificial_neural_networks_applied_to_mi.pdf&Expires=1593477539&Signature=DNmSxKogrD54dE4LX~8DT4K7vV0ZGcf8Q2RRfXEPsCc8PGiBrbeBpy4NVQdCiENLz-YfSzVGk6LI8k5MEGxR~qwnUn9ISLHDuIau6VqBFSEA29jMixCbvQM6hbkUJKQlli-AuSPUV23TsSk76kB6amDYtwNHmBnUPzTQGZLj2XkzJza9PA-7W2-VrPQKHNPxJp3z8J0mPq4rhmHZLaFMMSL6QMpK5qpvSqi6Znx-kIhCprlyYfODisq0unOIwnEQstiMf2RnB6gPmGOodhNlLsSr01e7TvtvFDBOQvhhooeDeQrvkINN4DJjAIIrbrcQ8B2b-ATQS0a3QQe93h-VFA__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA - Leite, EPL; de Souza Filho, CR Artificial neural networks applied to mineral potential mapping for copper-gold mineralizations in the Carajás Mineral Province, Brazil. Geoph. Prosp. 2009, 57, 1049–1065.
• https://link-springer-com.access.library.unisa.edu.au/content/pdf/10.1007/s11053-015-9263-2.pdf - A Comparative Analysis of Weights of Evidence, Evidential Belief Functions, and Fuzzy Logic for Mineral Potential Mapping Using Incomplete Data at the Scale of Investigation
• https://library.seg.org/doi/abs/10.1190/sbgf2011-245 - Gold Prospectivity Mapping of Andorinhas Greenstone Belt, Para

Gurupi

• https://www.researchgate.net/publication/312220651_Predictive_Mapping_of_Prospectivity_in_the_Gurupi_Orogenic_Gold_Belt_North-Northeast_Brazil_An_Example_of_District-Scale_Mineral_System_Approach_to_Exploration_Targeting

Austrália

• https://www.researchgate.net/publication/260107484_Unsupervised_clustering_of_continental-scale_geophysical_and_geochemical_data_using_Self-Organising_Maps
• https://www.researchgate.net/publication/332263305_A_speedy_update_on_machine_learning_applied_to_bedrock_mapping_using_geochemistry_or_geophysics_examples_from_the_Pacific_Rim_and_nearby
• https://www.researchgate.net/publication/317312520_Catchment-based_gold_prospectivity_analysis_combining_geochemical_geophysical_and_geological_data_across_northern_Australia
• https://www.researchgate.net/publication/326571155_Continental-scale_mineral_prospectivity_assessment_using_the_National_Geochemical_Survey_of_Australia_NGSA_dataset
• https://www.researchgate.net/publication/334440382_Mapping_iron_oxide_Cu-Au_IOCG_mineral_potential_in_Australia_using_a_knowledge-driven_mineral_systems-based_approach
• https://www.researchgate.net/publication/282189370_Uranium_Prospectivity_Mapping_Across_the_Australian_Continent_via_Unsupervised_Cluster_Analysis_of_Integrated_Remote_Sensing_Data

South Australia

• https://www.researchgate.net/publication/335313790_Prospectivity_modelling_of_the_Olympic_Cu-Au_Province - https://services.sarig.sa.gov.au/raster/ProspectivityModelling/wms?service=wms&version=1.1.1&REQUEST=GetCapabilities

Queensland

• https://www.researchgate.net/publication/317312520_Catchment-based_gold_prospectivity_analysis_combining_geochemical_geophysical_and_geological_data_across_northern_Australia
• https://www.researchgate.net/publication/252707107_GIS-based_epithermal_copper_prospectivity_mapping_of_the_Mt_Isa_Inlier_Australia_Implications_for_exploration_targeting
• https://www.researchgate.net/publication/222211452_Predictive_modelling_of_prospectivity_for_Pb-Zn_deposits_in_the_Lawn_Hill_Region_Queensland_Australia

Nova Gales do Sul

• https://www.researchgate.net/publication/336349643_MINERAL_POTENTIAL_MAPPING_AS_A_STRATEGIC_PLANNING_TOOL_IN_THE_EASTERN_LACHLAN_OROGEN_NSW
• https://www.publish.csiro.au/ex/pdf/ASEG2013ab236 - Mineral prospectivity analysis of the Wagga–Omeo belt in NSW
• https://www.researchgate.net/publication/329761040_NSW_Zone_54_Mineral_Systems_Mineral_Potential_Report
• https://www.researchgate.net/publication/337569823_Practical_Implementation_of_Random_Forest-Based_Mineral_Potential_Mapping_for_Porphyry_Cu-Au_Mineralization_in_the_Eastern_Lachlan_Orogen_NSW_Australia
• https://www.researchgate.net/publication/333551776_Translating_expressions_of_intrusion-related_mineral_systems_into_mappable_spatial_proxies_for_mineral_potential_mapping_Case_studies_from_the_Southern_New_England_Orogen_Australia

Tasmania

• https://www.researchgate.net/publication/262380025_Mapping_geology_and_volcanic-hosted_massive_sulfide_alteration_in_the_Hellyer-Mt_Charter_region_Tasmania_using_Random_Forests_TM_and_Self-Organising_Maps

Vitória

• https://www.researchgate.net/publication/323856713_Lithological_mapping_using_Random_Forests_applied_to_geophysical_and_remote_sensing_data_a_demonstration_study_from_the_Eastern_Goldfields_of_Australia
• https://publications.csiro.au/publications/#publication/PIcsiro:EP123339/SQmineral%20prospectivity/RP1/RS50/RORECENT/STsearch-by-keyword/LISEA/RI16/RT26 [nickel]
• https://www.researchgate.net/publication/257026553_Regional_prospectivity_analysis_for_hydrothermal-remobilised_nickel_mineral_systems_in_western_Victoria_Australia

Western Australia

• https://www.researchgate.net/publication/274714146_Reducing_subjectivity_in_multi-commodity_mineral_prospectivity_analyses_Modelling_the_west_Kimberley_Australia
• https://www.researchgate.net/publication/319013132_Identifying_mineral_prospectivity_using_3D_magnetotelluric_potential_field_and_geological_data_in_the_east_Kimberley_Australia
• https://www.researchgate.net/publication/280930127_Regional-scale_targeting_for_gold_in_the_Yilgarn_Craton_Part_1_of_the_Yilgarn_Gold_Exploration_Targeting_Atlas
• https://www.researchgate.net/publication/279533541_District-scale_targeting_for_gold_in_the_Yilgarn_Craton_Part_2_of_the_Yilgarn_Gold_Exploration_Targeting_Atlas
• https://www.researchgate.net/publication/257026568_Exploration_targeting_for_orogenic_gold_deposits_in_the_Granites-Tanami_Orogen_Mineral_system_analysis_targeting_model_and_prospectivity_analysis
• https://www.researchgate.net/publication/280039091_Mineral_systems_approach_applied_to_GIS-based_2D-prospectivity_modelling_of_geological_regions_Insights_from_Western_Australia (the West Arunta Orogen, West Musgrave Orogen and Gascoyne Province - http://dmpbookshop.eruditetechnologies.com.au/product/mineral-systems-analysis-of-the-west-musgrave-province-regional-structure-and-prospectivity-modelling.do
• https://reader.elsevier.com/reader/sd/pii/S0169136810000417? - token=9FD1C06A25E7ECC0C384C0ECF976E4BC9C36047C53CEED08066811979A640E89DD94C49510D1B500C6FF5E69982E018E Prospectivity analysis of the Plutonic Marymia Greenstone Belt, Western Australia
• https://research-repository.uwa.edu.au/en/publications/exploration-targeting-for-orogenic-gold-deposits-in-the-granites- - Tanami orogen
• https://www.researchgate.net/publication/332631130_Fuzzy_inference_systems_for_prospectivity_modeling_of_mineral_systems_and_a_case-study_for_prospectivity_mapping_of_surficial_Uranium_in_Yeelirrie_Area_Western_Australia_Ore_Geology_Reviews_71_839-852Tasmania
• https://publications.csiro.au/rpr/download?pid=csiro:EP102133&dsid=DS3 [nickel]

Endowment Modelling

• https://www.researchgate.net/publication/248211962_A_new_method_for_spatial_centrographic_analysis_of_mineral_deposit_clusters
• https://www.researchgate.net/publication/275620329_A_Time-Series_Audit_of_Zipf's_Law_as_a_Measure_of_Terrane_Endowment_and_Maturity_in_Mineral_Exploration
• https://www.researchgate.net/publication/341087909_Assessing_the_variability_of_expert_estimates_in_the_USGS_Three-part_Mineral_Resource_Assessment_Methodology_A_call_for_increased_skill_diversity_and_scenario-based_training
• https://github.com/iagoslc/ZipfsLaw_Quadrilatero_Ferrifero
• https://www.researchgate.net/publication/222834436_Controls_on_mineral_deposit_occurrence_inferred_from_analysis_of_their_spatial_pattern_and_spatial_association_with_geological_features
• https://www.researchgate.net/publication/229792860_From_Predictive_Mapping_of_Mineral_Prospectivity_to_Quantitative_Estimation_of_Number_of_Undiscovered_Prospects
• https://www.researchgate.net/publication/330994502_Global_Grade-and-Tonnage_Modeling_of_Uranium_deposits
• https://pubs.geoscienceworld.org/segweb/economicgeology/article-abstract/103/4/829/127993/Linking-Mineral-Deposit-Models-to-Quantitative?redirectedFrom=fulltext
• https://www.researchgate.net/publication/238365283_Metal_endowment_of_cratons_terranes_and_districts_Insights_from_a_quantitative_analysis_of_regions_with_giant_and_super-giant_deposits
• https://www.researchgate.net/publication/308778798_Spatial_analysis_of_mineral_deposit_distribution_A_review_of_methods_and_implications_for_structural_controls_on_iron_oxide-copper-gold_mineralization_in_Carajas_Brazil
• https://www.researchgate.net/publication/229347041_Predictive_mapping_of_prospectivity_and_quantitative_estimation_of_undiscovered_VMS_deposits_in_Skellefte_district_Sweden
• https://www.researchgate.net/publication/342405763_Predicting_grade-tonnage_characteristics_of_undiscovered_mineralisation_application_of_the_USGS_Three-part_Undiscovered_Mineral_Resource_Assessment_to_the_Sandstone_Greenstone_Belt_of_the_Yilgarn_Bloc
• https://www.sciencedirect.com/science/article/pii/S0169136810000685
• https://www.researchgate.net/publication/240301743_Spatial_statistical_analysis_of_the_distribution_of_komatiite-hosted_nickel_sulfide_deposits_in_the_Kalgoorlie_terrane_Western_Australia_Clustered_or_Not

World Models

• https://www.researchgate.net/publication/331283650_Archean_crust_and_metallogenic_zones_in_the_Amazonian_Craton_sensed_by_satellite_gravity_data
• https://eartharxiv.org/2kjvc/ -> Global distribution of sediment-hosted metals controlled by craton edge stability
• https://www.researchgate.net/post/Is_it_possible_to_derive_free_air_anomaly_or_bouguer_anomaly_from_gravity_disturbance_data
• https://www.researchgate.net/publication/325344128_The_role_of_basement_control_in_Iron_Oxide-Copper-Gold_mineral_systems_revealed_by_satellite_gravity_models
• https://www.researchgate.net/publication/331428028_Supplementary_Material_for_the_paper_Archean_crust_and_metallogenic_zones_in_the_Amazonian_Craton_sensed_by_satellite_gravity_data
• https://www.leouieda.com/pdf/use-the-disturbance.pdf
• https://www.leouieda.com/papers/use-the-disturbance.html

Financial Forecasting

• https://www.researchgate.net/publication/317137060_Forecasting_copper_prices_by_decision_tree_learning
• https://www.researchgate.net/publication/4874824_Mine_Size_and_the_Structure_of_Costs

Agent based Modelling

• https://mpra.ub.uni-muenchen.de/62159/ -> Mineral exploration as a game of chance [Agent Based Modelling]

Spectral Unmixing

• Overviews and examples, with some focus on neural network approaches.

Redes Neurais

• https://www.researchgate.net/publication/224180646_A_neural_network_approach_for_pixel_unmixing_in_hyperspectral_data
• https://www.researchgate.net/publication/340690859_A_Supervised_Nonlinear_Spectral_Unmixing_Method_by_Means_of_Neural_Networks
• https://www.researchgate.net/publication/326205017_Classification_of_Hyperspectral_Data_Using_a_Multi-Channel_Convolutional_Neural_Network
• https://www.researchgate.net/publication/339062151_Classification_of_small-scale_hyperspectral_images_with_multi-source_deep_transfer_learning
• https://www.researchgate.net/publication/331824337_Comparative_Analysis_of_Unmixing_Algorithms_Using_Synthetic_Hyperspectral_Data
• https://www.researchgate.net/publication/335501086_Convolutional_Autoencoder_For_Spatial-Spectral_Hyperspectral_Unmixing
• https://www.researchgate.net/publication/341501560_Convolutional_Autoencoder_for_Spectral-Spatial_Hyperspectral_Unmixing
• https://www.researchgate.net/publication/333906204_Deep_convolutional_neural_networks_for_land-cover_classification_with_Sentinel-2_images
• https://www.researchgate.net/publication/356711693_Deep-learning-based_latent_space_encoding_for_spectral_unmixing_of_geological_materials
• https://www.researchgate.net/publication/331505001_Deep_learning_and_its_application_in_geochemical_mapping
• https://www.researchgate.net/publication/332696102_Deep_Learning_for_Classification_of_Hyperspectral_Data_A_Comparative_Review
• https://www.researchgate.net/publication/336889271_Deep_Learning_for_Hyperspectral_Image_Classification_An_Overview
• https://www.researchgate.net/publication/327995228_Deep_Spectral_Convolution_Network_for_Hyperspectral_Unmixing
• https://ieeexplore.ieee.org/abstract/document/10580951 -> Exploring Hybrid Contrastive Learning and Scene-to-Label Information for Multilabel Remote Sensing Image Classification [UNSEEN]
• https://www.researchgate.net/publication/356393038_Generalized_Unsupervised_Clustering_of_Hyperspectral_Images_of_Geological_Targets_in_the_Near_Infrared
• https://ieeexplore.ieee.org/abstract/document/10588073 -> Hyperspectral Image Classification Using Spatial and Spectral Features Based on Deep Learning [UNSEEN]
• https://www.researchgate.net/publication/333301728_Hyperspectral_Image_Classification_Method_Based_on_CNN_Architecture_Embedding_With_Hashing_Semantic_Feature
• https://www.researchgate.net/publication/323950012_Hyperspectral_Unmixing_Using_A_Neural_Network_Autoencoder
• https://www.researchgate.net/publication/339657313_Hyperspectral_unmixing_using_deep_convolutional_autoencoder
• https://www.researchgate.net/publication/339066136_Hyperspectral_Unmixing_Using_Deep_Convolutional_Autoencoders_in_a_Supervised_Scenario
• https://www.researchgate.net/publication/335878933_LITHOLOGICAL_CLASSIFICATION_USING_MULTI-SENSOR_DATA_AND_CONVOLUTIONAL_NEURAL_NETWORKS
• https://ieeexplore.ieee.org/abstract/document/10551851 -> MSNet: Self-Supervised Multiscale Network With Enhanced Separation Training for Hyperspectral Anomaly Detection
• https://www.researchgate.net/publication/331794887_Nonlinear_Unmixing_of_Hyperspectral_Data_via_Deep_Autoencoder_Networks
• https://ieeexplore.ieee.org/abstract/document/10534107 -> ReSC-net: Hyperspectral Image Classification Based on Attention-Enhanced Residual Module and Spatial-Channel Attention
• https://www.researchgate.net/publication/340961027_Recent_Advances_in_Hyperspectral_Unmixing_Using_Sparse_Techniques_and_Deep_Learning
• https://www.researchgate.net/publication/330272600_Semisupervised_Stacked_Autoencoder_With_Cotraining_for_Hyperspectral_Image_Classification
• https://www.researchgate.net/publication/336097421_Spatial-Spectral_Hyperspectral_Unmixing_Using_Multitask_Learning
• https://www.researchgate.net/publication/312355586_Spectral-Spatial_Classification_of_Hyperspectral_Imagery_with_3D_Convolutional_Neural_Network
• https://meetingorganizer.copernicus.org/EGU2020/EGU2020-10719.html -> Sentinel-2 as a tool for mapping iron-bearing alteration minerals: a case study from the Iberian Pyrite Belt (Southern Spain)
• https://www.researchgate.net/publication/334058881_SSDC-DenseNet_A_Cost-Effective_End-to-End_Spectral-Spatial_Dual-Channel_Dense_Network_for_Hyperspectral_Image_Classification
• https://www.researchgate.net/publication/333497470_Integration_of_auto-encoder_network_with_density-based_spatial_clustering_for_geochemical_anomaly_detection_for_mineral_exploration
• https://www.sciencedirect.com/science/article/pii/S0009281924000473 -> Geochemical characteristics and mapping of Reşadiye (Tokat-Türkiye) bentonite deposits using machine learning and sub-pixel mixture algorithms

Em geral

• https://www.sciencedirect.com/science/article/pii/S0273117724004861?dgcid=rss_sd_all -> Optimization of machine learning algorithms for remote alteration mapping
• https://www.researchgate.net/publication/337841253_A_solar_optical_hyperspectral_library_of_rare_earth-bearing_minerals_rare_earth_oxides_copper-bearing_minerals_and_Apliki_mine_surface_samples
• https://ieeexplore.ieee.org/document/10536904 -> A Reversible Generative Network for Hyperspectral Unmixing With Spectral Variability
• https://www.researchgate.net/publication/3204295_Abundance_Estimation_of_Spectrally_Similar_Minerals_by_Using_Derivative_Spectra_in_Simulated_Annealing
• https://www.researchgate.net/publication/338371376_Accuracy_assessment_of_hydrothermal_mineral_maps_derived_from_ASTER_images
• https://www.researchgate.net/publication/337790490_Analysis_of_Most_Significant_Bands_and_Band_Ratios_for_Discrimination_of_Hydrothermal_Alteration_Minerals
• https://www.researchgate.net/project/Deep-Learning-for-Remote-Sensing-2
• https://ieeexplore.ieee.org/abstract/document/10589462 -> Deep Spectral Spatial Feature Enhancement through Transformer for Hyperspectral Image Classification
• https://www.researchgate.net/publication/331876006_Fusion_of_Landsat_and_Worldview_Images
• https://www.researchgate.net/publication/259096595_Geological_mapping_using_remote_sensing_data_A_comparison_of_five_machine_learning_algorithms_their_response_to_variations_in_the_spatial_distribution_of_training_data_and_the_use_of_explicit_spatial_
• https://www.researchgate.net/publication/341802637_Improved_k-means_and_spectral_matching_for_hyperspectral_mineral_mapping
• https://www.researchgate.net/publication/272565561_Integration_and_Analysis_of_ASTER_and_IKONOS_Images_for_the_Identification_of_Hydrothermally-_Altered_Mineral_Exploration_Sites
• https://www.researchgate.net/publication/236271149_Multi-_and_hyperspectral_geologic_remote_sensing_A_review_GRSG_Member_News
• https://www.researchgate.net/publication/220492175_Multi-and_Hyperspectral_geologic_remote_sensing_A_review
• https://www.sciencedirect.com/science/article/pii/S1574954124001572 -> Rapid estimation of soil Mn content by machine learning and soil spectra in large-scale
• https://www.researchgate.net/publication/342184377_remotesensing-12-01239-v2_1
• https://www.researchgate.net/project/Remote-sensing-exploration-of-critical-mineral-deposits
• https://www.researchgate.net/project/Sentinel-2-MSI-for-geological-remote-sensing
• https://www.researchgate.net/publication/323808118_Thermal_infrared_multispectral_remote_sensing_of_lithology_and_mineralogy_based_on_spectral_properties_of_materials
• https://www.researchgate.net/publication/340505978_Unsupervised_and_Supervised_Feature_Extraction_Methods_for_Hyperspectral_Images_Based_on_Mixtures_of_Factor_Analyzers

África

• https://www.researchgate.net/publication/235443308_Application_of_remote_sensing_and_GIS_mapping_to_Quaternary_to_recent_surficial_sediments_of_the_Central_Uranium_district_Namibia
• https://www.researchgate.net/publication/342373512_Geological_mapping_using_Random_Forests_applied_to_Remote_Sensing_data_a_demonstration_study_from_Msaidira-Souk_Al_Had_Sidi_Ifni_inlier_Western_Anti-Atlas_Morocco
• https://www.researchgate.net/publication/340534611_Identifying_high_potential_zones_of_gold_mineralization_in_a_sub-tropical_region_using_Landsat-8_and_ASTER_remote_sensing_data_a_case_study_of_the_Ngoura-Colomines_goldfield_Eastern_Cameroon
• https://www.researchgate.net/publication/342162988_Lithological_and_alteration_mineral_mapping_for_alluvial_gold_exploration_in_the_south_east_of_Birao_area_Central_African_Republic_using_Landsat-8_Operational_Land_Imager_OLI_data
• https://www.researchgate.net/publication/329193841_Mapping_Copper_Mineralisation_using_EO-1_Hyperion_Data_Fusion_with_Landsat_8_OLI_and_Sentinel-2A_in_Moroccan_Anti_Atlas
• https://www.researchgate.net/publication/230918249_SPECTRAL_REMOTE_SENSING_OF_HYDROTHERMAL_ALTERATION_ASSOCIATED_WITH_VOLCANOGENIC_MASSIVE_SULPHIDE_DEPOSITS_GOROB-HOPE_AREA_NAMIBIA
• https://www.researchgate.net/publication/337304180_The_application_of_day_and_night_time_ASTER_satellite_imagery_for_geothermal_and_mineral_mapping_in_East_Africa
• https://www.researchgate.net/publication/336823002_Towards_Multiscale_and_Multisource_Remote_Sensing_Mineral_Exploration_Using_RPAS_A_Case_Study_in_the_Lofdal_Carbonatite-Hosted_REE_Deposit_Namibia
• https://www.researchgate.net/publication/338296843_Use_of_the_Sentinel-2A_Multispectral_Image_for_Litho-Structural_and_Alteration_Mapping_in_Al_Glo'a_Map_Sheet_150000_Bou_Azzer-El_Graara_Inlier_Central_Anti-Atlas_Morocco

Brasil

• https://www.researchgate.net/publication/287950835_Altimetric_and_aeromagnetometric_data_fusion_as_a_tool_of_geological_interpretation_the_example_of_the_Carajas_Mineral_Province_PA
• https://www.researchgate.net/publication/237222985_Analise_e_integracao_de_dados_do_SAR-R99B_com_dados_de_sensoriamento_remoto_optico_e_dados_aerogeofisicos_na_regiao_dos_depositos_de_oxido_de_Fe-Cu-Au_tipo_Sossego_e_118_na_Provincia_Mineral_de_Caraja
• https://www.researchgate.net/publication/327503453_Comparison_of_Altered_Mineral_Information_Extracted_from_ETM_ASTER_and_Hyperion_data_in_Aguas_Claras_Iron_Ore_Brazil
• https://www.researchgate.net/publication/251743903_Enhancement_Of_Landsat_Thematic_Mapper_Imagery_For_Mineral_Prospecting_In_Weathered_And_Vegetated_Terrain_In_SE_Brazil
• https://www.researchgate.net/publication/228854234_Hyperspectral_Data_Processing_For_Mineral_Mapping_Using_AVIRIS_1995_Data_in_Alto_Paraiso_de_Goias_Central_Brazil
• https://www.researchgate.net/publication/326612136_Mapping_Mining_Areas_in_the_Brazilian_Amazon_Using_MSISentinel-2_Imagery_2017
• https://www.researchgate.net/publication/242188704_MINERALOGICAL_CHARACTERIZATION_AND_MAPPING_USING_REFLECTANCE_SPECTROSCOPY_AN_EXPERIMENT_AT_ALTO_DO_GIZ_PEGMATITE_IN_THE_SOUTH_PORTION_OF_BORBOREMA_PEGMATITE_PROVINCE_BPP_NORTHEASTERN_BRAZIL

China

• https://www.researchgate.net/publication/338355143_A_comprehensive_scheme_for_lithological_mapping_using_Sentinel-2A_and_ASTER_GDEM_in_weathered_and_vegetated_coastal_zone_Southern_China
• https://www.researchgate.net/publication/332957713_Data_mining_of_the_best_spectral_indices_for_geochemical_anomalies_of_copper_A_study_in_the_northwestern_Junggar_region_Xinjiang
• https://www.researchgate.net/publication/380287318_Machine_learning_model_for_deep_exploration_Utilizing_short_wavelength_infrared_SWIR_of_hydrothermal_alteration_minerals_in_the_Qianchen_gold_deposit_Jiaodong_Peninsula_Eastern_China
• https://www.researchgate.net/publication/304906898_Remote_sensing_and_GIS_prospectivity_mapping_for_magmatic-hydrothermal_base-_and_precious-metal_deposits_in_the_Honghai_district_China

Groenlândia

• https://www.researchgate.net/publication/326655551_Application_of_Multi-Sensor_Satellite_Data_for_Exploration_of_Zn-Pb_Sulfide_Mineralization_in_the_Franklinian_Basin_North_Greenland
• https://www.researchgate.net/publication/337512735_Fusion_of_DPCA_and_ICA_algorithms_for_mineral_detection_using_Landsat-8_spectral_bands
• https://www.researchgate.net/publication/336684298_Landsat-8_Advanced_Spaceborne_Thermal_Emission_and_Reflection_Radiometer_and_WorldView-3_Multispectral_Satellite_Imagery_for_Prospecting_Copper-Gold_Mineralization_in_the_Northeastern_Inglefield_Mobil

Índia

• https://www.researchgate.net/publication/337649256_Automated_lithological_mapping_by_integrating_spectral_enhancement_techniques_and_machine_learning_algorithms_using_AVIRIS-NG_hyperspectral_data_in_Gold-bearing_granite-greenstone_rocks_in_Hutti_India
• https://www.researchgate.net/publication/333816841_Integrated_application_of_AVIRIS-NG_and_Sentinel-2A_dataset_in_altered_mineral_abundance_mapping_A_case_study_from_Jahazpur_area_Rajasthan
• https://www.researchgate.net/publication/339631389_Identification_and_characterization_of_hydrothermally_altered_minerals_using_surface_and_space-based_reflectance_spectroscopy_in_parts_of_south-eastern_Rajasthan_India
• https://www.researchgate.net/publication/338116272_Potential_Use_of_ASTER_Derived_Emissivity_Thermal_Inertia_and_Albedo_Image_for_Discriminating_Different_Rock_Types_of_Aravalli_Group_of_Rocks_Rajasthan

Irã

• https://www.researchgate.net/publication/338336181_A_Remote_Sensing-Based_Application_of_Bayesian_Networks_for_Epithermal_Gold_Potential_Mapping_in_Ahar-Arasbaran_Area_NW_Iran
• https://www.researchgate.net/publication/338371376_Accuracy_assessment_of_hydrothermal_mineral_maps_derived_from_ASTER_images
• https://www.researchgate.net/publication/340606566_Application_of_Landsat-8_Sentinel-2_ASTER_and_WorldView-3_Spectral_Imagery_for_Exploration_of_Carbonate-Hosted_Pb-Zn_Deposits_in_the_Central_Iranian_Terrane_CIT
• https://www.researchgate.net/publication/331428927_Comparison_of_Different_Algorithms_to_Map_Hydrothermal_Alteration_Zones_Using_ASTER_Remote_Sensing_Data_for_Polymetallic_Vein-Type_Ore_Exploration_Toroud-Chahshirin_Magmatic_Belt_TCMB_North_Iran
• https://www.researchgate.net/publication/327832371_Band_Ratios_Matrix_Transformation_BRMT_A_Sedimentary_Lithology_Mapping_Approach_Using_ASTER_Satellite_Sensor
• https://www.researchgate.net/publication/331314687_Lithological_mapping_in_Sangan_region_in_Northeast_Iran_using_ASTER_satellite_data_and_image_processing_methods
• https://www.researchgate.net/publication/330774780_Mapping_hydrothermal_alteration_zones_and_lineaments_associated_with_orogenic_gold_mineralization_using_ASTER_data_A_case_study_from_the_Sanandaj-Sirjan_Zone_Iran
• https://www.researchgate.net/publication/380812370_Optimization_of_machine_learning_algorithms_for_remote_alteration_mapping
• https://www.researchgate.net/publication/362620968_Spatial_mapping_of_hydrothermal_alterations_and_structural_features_for_gold_and_cassiterite_exploration

Peru

• https://www.researchgate.net/publication/271714561_Geology_and_Hydrothermal_Alteration_of_the_Chapi_Chiara_Prospect_and_Nearby_Targets_Southern_Peru_Using_ASTER_Data_and_Reflectance_Spectroscopy
• https://www.researchgate.net/publication/317141295_Hyperspectral_remote_sensing_applied_to_mineral_exploration_in_southern_Peru_A_multiple_data_integration_approach_in_the_Chapi_Chiara_gold_prospect

Espanha

• https://www.researchgate.net/publication/233039694_Geological_mapping_using_Landsat_Thematic_Mapper_imagery_in_Almeria_Province_south-east_Spain
• https://www.researchgate.net/publication/263542786_WEIGHTS_DERIVED_FROM_HYPERSPECTRAL_DATA_TO_FACILITATE_AN_OPTIMAL_FIELD_SAMPLING_SCHEME_FOR_POTENTIAL_MINERALS

Outro

• https://www.researchgate.net/publication/341611032_ASTER_spectral_band_ratios_for_lithological_mapping_A_case_study_for_measuring_geological_offset_along_the_Erkenek_Segment_of_the_East_Anatolian_Fault_Zone_Turkey

• https://www.researchgate.net/publication/229383008_Hydrothermal_Alteration_Mapping_at_Bodie_California_using_AVIRIS_Hyperspectral_Data

• https://www.researchgate.net/publication/332737573_Identification_of_alteration_zones_using_a_Landsat_8_image_of_densely_vegetated_areas_of_the_Wayang_Windu_Geothermal_field_West_Java_Indonesia

• https://www.researchgate.net/publication/325137721_Interpretation_of_surface_geochemical_data_and_integration_with_geological_maps_and_Landsat-TM_images_for_mineral_exploration_from_a_portion_of_the_precambrian_of_Uruguay

• https://www.researchgate.net/publication/336684298_Landsat-8_Advanced_Spaceborne_Thermal_Emission_and_Reflection_Radiometer_and_WorldView-3_Multispectral_Satellite_Imagery_for_Prospecting_Copper-Gold_Mineralization_in_the_Northeastern_Inglefield_Mobil

• https://www.researchgate.net/publication/304036250_Mineral_Exploration_for_Epithermal_Gold_in_Northern_Patagonia_Argentina_From_Regional-_to_Deposit-Scale_Prospecting_Using_Landsat_TM_and_Terra_ASTER

• https://www.researchgate.net/publication/340652300_New_logical_operator_algorithms_for_mapping_of_hydrothermally_altered_rocks_using_ASTER_data_A_case_study_from_central_Turkey

• https://www.researchgate.net/publication/324938267_Regional_geology_mapping_using_satellite-based_remote_sensing_approach_in_Northern_Victoria_Land_Antarctica

• https://www.researchgate.net/publication/379960654_From_sensor_fusion_to_knowledge_distillation_in_collaborative_LIBS_and_hyperspectral_imaging_for_mineral_identification

PNL

• https://ieeexplore.ieee.org/abstract/document/10544642 -> Assessing named entity recognition efficacy using diverse geoscience datasets [UNSEEN]
• https://link.springer.com/article/10.1007/s12371-024-01011-2 -> Can AI Get a Degree in Geoscience? Performance Analysis of a GPT-Based Artificial Intelligence System Trained for Earth Science (GeologyOracle)
• https://www.researchgate.net/publication/376671309_Enhancing_knowledge_discovery_from_unstructured_data_using_a_deep_learning_approach_to_support_subsurface_modeling_predictions
• https://www.mdpi.com/2220-9964/13/7/260 -> Extracting Geoscientific Dataset Names from the Literature Based on the Hierarchical Temporal Memory Model
• https://www.sciencedirect.com/science/article/pii/S0169136824002154 -> Three-dimensional mineral prospectivity mapping based on natural language processing and random forests: A case study of the Xiyu diamond deposit, China

LLM

• https://arxiv.org/pdf/2401.16822 - EarthGPT: A Universal Multi-modal Large Language Model for Multi-sensor Image Comprehension in Remote Sensing Domain
• https://link.springer.com/article/10.1007/s12371-024-01011-2 -> Can AI Get a Degree in Geoscience? Performance Analysis of a GPT-Based Artificial Intelligence System Trained for Earth Science (GeologyOracle)
  • Geology Oracle web prototype - https://geologyoracle.com/ask-the-geologyoracle/

General-Interest

• https://arxiv.org/abs/2404.05746v1 -> Causality for Earth Science -- A Review on Time-series and Spatiotemporal Causality Methods
• https://www.researchgate.net/publication/384137154_Guidelines_for_Sensitivity_Analyses_in_Process_Simulations_for_Solid_Earth_Geosciences
• https://www.mdpi.com/1660-4601/18/18/9752 -> Learning and Expertise in Mineral Exploration Decision-Making: An Ecological Dynamics Perspective
• https://www.sciencedirect.com/science/article/pii/S2214629624001476 -> Mapping critical minerals projects and their intersection with Indigenous peoples' land rights in Australia
• https://www.sciencedirect.com/science/article/pii/S0169136824003470 -> Overcoming survival bias in targeting mineral deposits of the future: Towards null and negative tests of the exploration search space, accounting for lack of visibility
• https://www.sciencedirect.com/science/article/pii/S088329272400115X - > Ranking Mineral Exploration Targets in Support of Commercial Decision Making: A Key Component for Inclusion in an Exploration Information System

Aprendizado profundo

• https://arxiv.org/abs/2408.11804 -> Approaching Deep Learning through the Spectral Dynamics of Weights
• https://arxiv.org/pdf/2310.19909.pdf -> Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks
• https://pure.mpg.de/rest/items/item_3029184_8/component/file_3282959/content -> Deep learning and process understanding for data-driven Earth system science
• https://www.tandfonline.com/doi/pdf/10.1080/17538947.2024.2391952 -> Deep learning for spatiotemporal forecasting in Earth system science: a review
• https://wires.onlinelibrary.wiley.com/doi/pdfdirect/10.1002/widm.1554 -> From 3D point-cloud data to explainable geometric deep learning: State-of-the-art and future challenges
• https://arxiv.org/pdf/2410.16602 -> Foundation Models for Remote Sensing and Earth Observation: A Survey
• https://www.nature.com/articles/s41467-021-24025-8 -> Predicting trends in the quality of state-of-the-art neural networks without access to training or testing data
• https://arxiv.org/abs/2404.07738 ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models
• https://ieeexplore.ieee.org/abstract/document/10605826 -> Swin-CDSA: The Semantic Segmentation of Remote Sensing Images Based on Cascaded Depthwise Convolution and Spatial Attention Mechanism
• https://www.sciencedirect.com/science/article/abs/pii/S0098300424000839#sec6 -> Leveraging automated deep learning (AutoDL) in geosciences