mineral exploration machine learning

pembelajaran mesin-eksplorasi-mineral

Halaman ini mencantumkan sumber daya untuk eksplorasi mineral dan pembelajaran mesin, umumnya dengan kode dan contoh yang berguna. ML dan Ilmu Data adalah bidang yang sangat besar, ini adalah sumber daya yang menurut saya berguna dan/atau menarik bagi saya dalam praktiknya. Tautan saat ini ke cabang repositori adalah karena saya telah mengubah sesuatu untuk digunakan dan dimasukkan ke dalam daftar untuk referensi. Sumber daya juga diberikan untuk analisis data, transformasi, dan visualisasi karena ini merupakan sebagian besar pekerjaan.

Saran diterima: buka diskusi, masalah, atau permintaan tarik.

Daftar isi

• Prospektivitas
• Geologi
• Pemrosesan Bahasa Alami
• Penginderaan Jauh
• Kualitas Data
• Masyarakat
• Penyedia awan
• Domain
• Ringkasan
• Layanan Web
• Portal Data
• Peralatan
• Ontologi
• Buku
• Kumpulan data
• Dokumen
• Lainnya
• Kepentingan Umum

Peta

• Peta Github

Kerangka kerja

• Kerangka UNCOVER-ML
  • Geo-Wavelet
  • ML-Pemrosesan awal
  • Alur Kerja GIS ML
• EIS Toolkit -> Pustaka Python untuk pemetaan prospektivitas mineral dari Proyek EIS Horizon EU
• PySpatialML -> Library yang memfasilitasi prediksi dan penanganan pembelajaran mesin raster secara otomatis ke geotiff, dll.
• peta scikit
• TorchGeo -> Perpustakaan Pytorch untuk model gaya penginderaan jauh
• terratorch -> Kerangka kerja penyesuaian yang fleksibel untuk Model Fondasi Geospasial
• OborSpasial
  • kertas
• geodl
• Geo Deep Learning -> Kerangka pembelajaran mendalam sederhana berdasarkan RGB
• AIDE: Kecerdasan Buatan untuk Menguraikan Hal-hal Ekstrem
• ExPloRA -> ExPLoRA: Pra-pelatihan yang Diperluas dengan Parameter-Efisien untuk Mengadaptasi Vision Transformers dalam Pergeseran Domain
  • Situs web
  • kertas
• (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-Kecerdasan-buatan-untuk-menguraikan-peristiwa-ekstrim.pdf)

R

• CAST -> Aplikasi Caret untuk model Spatio-Temporal
• geodl -> segmentasi semantik data geospasial menggunakan pembelajaran mendalam berbasis jaringan saraf konvolusional
  • kertas

Saluran pipa

• geotargts -> Perluasan target ke terra dan bintang

Prospektivitas

Australia

• Peta potensi mineral tembaga-emas besi oksida
• Pembelajaran mesin untuk pemetaan geologi : algoritma dan aplikasi -> Tesis PhD dengan kode dan data
• Pemetaan Prospektivitas Ni-Co Laterit
• Tutorial Transformasi 2022 -> Contoh hutan acak
  • Video
• Timah-Tungsten
  • Kolaborasi
• Eksplorasi Spatio-Temporal Tembaga Porfiri
  • kertas
• minpot-toolkit -> Contoh analisis Batas Lab Hoggard dkk dengan tembaga sedimen
• MPM-WofE -> Pemetaan Potensi Mineral - Bobot Bukti

Tantangan Penjelajah

• Explorer Challenge -> OZ Minerals menjalankan kompetisi dengan pengenalan Ilmu Data

Australia Selatan

• Gawler_MPM -> Cobalt, Kromium, Nikel
  • Kertas
• Pengelompokan Data Geofisika di Gawler Craton
  • [Data Zenodo](Deteksi otomatis struktur kraton terkait mineralisasi menggunakan data geofisika dan pembelajaran mesin tanpa pengawasan)

Jelajahi SA - Kompetisi Departemen Energi dan Pertambangan Australia Selatan

• Pemenang -> Informasi data SARIG
• Kaldera -> Analisis Kaldera Analytics
• Data Incerto
• Butterworth dan Barnett -> Entri Butterworth dan Barnett
• Pemetaan Mineralisasi Berbasis Data

Amerika Utara

Kanada

• Transfer Prospektivitas Pembelajaran
  • makalah -> Pemetaan prospektivitas mineral tipe porfiri dengan data yang tidak seimbang melalui pembelajaran transfer geologi sebelumnya

Amerika Selatan

• Pembelajaran mesin untuk mengklasifikasikan endapan bijih berdasarkan sifat tektonomagmatik

Brazil

• Mapa Preditivo -> proyek pelajar Brasil
• Course_Predictive_Mapping_USP -> Proyek Kursus
• Pemetaan Prospektivitas Mineral
• Bobot Bukti 3D
• Kompleksitas Geologi SMOTE -> mencakup analisis fraktal
  • kertas
• MPM Jurena -> Provinsi Mineral Jurena

Cina

• MPM dengan pembelajaran ansambel -> Distrik polimetalik Qingchengzi Pb-Zn-Ag-Au Cina
• Jaringan Neural Konvolusional Prediksi Prospektivitas Mineral -> Contoh CNN dengan beberapa arsitektur [makalah oleh penulis ini menggunakan GoogleNet]
• Prediksi Prospektivitas Mineral oleh CSAE
• Prediksi Prospektivitas Mineral oleh CAE
  • kertas

Sudan

• Pemetaan Prospektivitas Mineral ML

Norwegia

• Pendekatan berbasis pembelajaran mesin untuk pemetaan skala regional dari tanah liat glaciomarine sensitif yang menggabungkan data elektromagnetik dan geoteknik di udara
  • kertas

Geologi

• Peta Geologi Prediktif Brasil -> Karya survei geologi Brasil
• kedalaman hingga batuan dasar (Mengevaluasi pendekatan pembelajaran mesin yang diaktifkan secara spasial untuk pemetaan kedalaman hingga batuan dasar)
  • kertas
• DL-RMD -> Database model resistivitas elektromagnetik yang dibatasi secara geofisika untuk aplikasi pembelajaran mendalam
• Pengklasifikasi Gambar Geologi
• Pemetaan geologi di era kecerdasan buatan -> Pemetaan geologi di era kecerdasan buatan
• GeolNR -> Representasi Neural Implisit Geologi untuk aplikasi pemodelan geologi struktural tiga dimensi
  • kertas
• mapeamento_litologico_preditivo
• Memetakan Kondisi Suhu-Tekanan Mantel Litosfer Global dengan Termobarometri Pembelajaran Mesin
  • kertas
• Pengetikan Batu Neural
• Ketidakpastian Geologi Musgraves Barat -> Prediksi peta ketidakpastian dengan analisis entropi: sangat berguna
• Transformator Mitigasi Non Stasioneritas
  • Kolaborasi -> Buku Catatan
    • kertas
• Batuan dasar vs sedimen
  • kertas
• autoencoders_remotesensing
  • kertas -> Kerangka kerja penginderaan jauh untuk pemetaan geologi melalui autoencoder dan pengelompokan bertumpuk

Data Pelatihan

• Into the Noddyverse -> penyimpanan data besar-besaran model geologi 3D untuk pembelajaran mesin dan aplikasi inversi
  • Repositori Zenondo
  • situs web

Litologi

• Litologi Pembelajaran Mendalam
• Prediktor Protolit Batuan
• Prediksi Litologi Geologi SA
• Korelasi Log Sumur Otomatis
• dawson-facies-2022 -> Transfer pembelajaran untuk gambar geologi
  • makalah -> Dampak ukuran kumpulan data dan arsitektur jaringan saraf konvolusional pada pembelajaran transfer untuk klasifikasi batuan karbonat
• Litho-Classification -> Klasifikasi fasies vulkanik menggunakan Random Forest
• Pendekatan pembelajaran mesin ansambel multi-tampilan untuk pemodelan 3D menggunakan data geologi dan geofisika
  • kertas
• SedNet
  • kertas

Pengeboran

• Pengeboran Heterogen - Laporan proyek Nicta/Data61 untuk melihat pemodelan menggunakan lubang bor yang tidak cukup jauh
• corel -> model visi komputer pintar yang mengidentifikasi fasies dan melakukan pengetikan batu pada gambar inti

Lembah Paleoval

• Sub3DNet1.0: model pembelajaran mendalam untuk pemetaan struktur bawah permukaan 3D skala regional

Stratigrafi

• Predicatops -> Predikasi stratigrafi dirancang untuk hidrokarbon
• stratal-geometries -> Memprediksi Geometri Stratigrafi dari Log Sumur Bawah Permukaan

Struktural

• APGS -> Paket geologi struktural
• Menilai model rekonstruksi pelat menggunakan uji konsistensi gaya penggerak pelat -> Notebook dan data Jupyter
  • kertas
• secara gplately
• [buku masak geologi struktural](https://github.com/gcmatos/structural-geology-cookbook]
• GEOMAPLEARN 1.0 -> Mendeteksi struktur geologi dari peta geologi dengan pembelajaran mesin
• Lineament Learning -> Prediksi dan pemetaan kesalahan melalui pembelajaran mendalam dan pengelompokan bidang potensial
• LitMod3D -> Pemodelan interaktif geofisika-petrologi terintegrasi 3D dari litosfer dan mantel atas yang mendasarinya
• yang lain

Simulasi

• GebPy -> pembuatan data geologi untuk batuan dan mineral
• OpenGeoSys -> pengembangan metode numerik untuk simulasi proses termo-hidro-mekanis-kimia (THMC) pada media berpori dan retak
• Stratigraphics.jl -> Membuat stratigrafi 3D dari proses geostatistik 2D

Geodinamika

• Tanah tandus -> Dinamika Cekungan dan Lanskap
• CitcomS -> kode elemen hingga yang dirancang untuk memecahkan masalah konveksi termokimia kompresibel yang relevan dengan mantel bumi.
• LaMEM -> mensimulasikan berbagai proses geodinamik termo-mekanis seperti interaksi mantel-litosfer
• PTatin3D -> mempelajari proses skala waktu panjang yang relevan dengan geodinamika [motivasi asli: perangkat yang mampu mempelajari model deformasi litosfer tiga dimensi resolusi tinggi]
• dunia bawah -> Pemodelan elemen hingga geodinamika

Geofisika

Model Fondasi

• Adaptasi Model Landasan Lintas Domain: Perintis Model Visi Komputer untuk Analisis Data Geofisika -> beberapa kode yang akan datang
  • kertas
• Model Fondasi Seismik -> "model pembelajaran mendalam generasi baru dalam geofisika"

Australia

Kedalaman Regolit

• Kedalaman Regolit -> Model
• Lengkapi Radiometrics Grid of Australia dengan model pengisi

Interpolasi AEM

• Pemetaan konduktivitas resolusi tinggi menggunakan survei AEM regional
  • Abstrak

Elektromagnetik

• TEM-NLnet: Jaringan Denoising Dalam untuk Sinyal Elektromagnetik Sementara dengan Pembelajaran Kebisingan

Pembalikan

• Pembelajaran Mesin dan Inversi Geofisika -> merekonstruksi makalah dari Y. Kim dan N. Nakata (The Leading Edge, Volume 37, Edisi 12, Des 2018)

Dekonvolusi Euler

• https://legacy.fatiando.org/gallery/gravmag/euler_moving_window.html
  • Versi harmonika akhirnya? 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

Gaya berat

• [Memulihkan Relief Ruang Bawah Tanah 3D Menggunakan Data Gravitasi Melalui Jaringan Syaraf Konvolusional]
  • Data
  • kertas
• Kelanjutan bidang potensi gravitasi yang stabil ke bawah diimplementasikan menggunakan pembelajaran mendalam
  • kertas
• Pencitraan cepat untuk struktur kepadatan 3D dengan pendekatan pembelajaran mesin
  • kertas

Kemaknitan

• Peta aeromagnetik resolusi tinggi melalui Adapted-SRGAN
  • kertas
• MagImage2Geo3D
  • kertas

Seismik

• StorSeismic -> Pendekatan untuk melatih jaringan saraf terlebih dahulu untuk menyimpan fitur data seismik
• PINNtomo -> Tomografi seismik menggunakan jaringan saraf berbasis fisika
  • kertas

Seismologi

• obspy -> kerangka untuk pemrosesan seismologis

Petrofisika

• ML4Rocks -> Beberapa intro berfungsi

Tektonik

• Pelajari pelepasan lempengan subduksi di zona subduksi kuno menggunakan pembelajaran mesin -> Notebook
  • berbagi gambar
• Notebook Colab -> File input Google Colab untuk hasil benchmark publikasi ML-SEISMIC
  • kertas
• Melepaskan kekuatan Pembelajaran Mesin dalam Geodinamika
  • Terkait Tesis Kehormatan
• Jaringan Neural yang Diinformasikan Fisika untuk simulasi slip kesalahan dengan hukum gesekan laju dan keadaan
  • simulasi dan estimasi parameter gesekan pada kejadian slow slip
  • makalah -> Pembelajaran Mendalam Berbasis Fisika untuk Memperkirakan Distribusi Spasial Parameter Gesekan di Daerah Slip Lambat

Geokimia

• CODAinPractice -> Analisis Data Komposisi dalam Praktek
• GeoCoDa
• DAN-GRF -> Jaringan autoencoder dalam yang terhubung ke hutan acak geografis untuk deteksi anomali geokimia yang sadar spasial
  • kertas
• Prospeksi Geokimia Dash -> Aplikasi web mengklasifikasikan sedimen sungai dengan K-means
• Meningkatkan termobarometri pembelajaran mesin untuk magma yang mengandung klinopiroksen
  • kertas -> Meningkatkan-ML-Termobarometri-untuk-Klinopyroxene-Bearing-Magma
• Model kesuburan zirkon -> Pohon keputusan untuk memprediksi kesuburan zirkon dari endapan tembaga porfiri
  • kertas
• Alat Elemen Jejak Zirkon Pembelajaran Mesin untuk Memprediksi Jenis Deposit Porfiri dan Ukuran Sumber Daya
  • arsip zenodo
  • kertas
• geologi_class0 -> Pendekatan pembelajaran mesin untuk diskriminasi batuan beku dan endapan bijih dengan elemen jejak zirkon
  • kertas
  • Aplikasi demo
  • https://colab.research.google.com/drive/1-bOZgG6Nxt2Rp1ueO1SYmzIqCRiyyYcT
• GeokimiaCetak
  • kertas
• Geokimia global
• ICBMS Jacobina -> Analisis kimia pirit dari deposit emas
• Interpretasi Kimia Elemen Jejak Zirkon dari Bor dan Cukaru Peki: Pendekatan Konvensional dan Klasifikasi Hutan Acak
• indikator_minerals -> Bisakah PCA menceritakan kisah asal muasal turmalin?
• Jurnal Eksplorasi Geokimia - Manifold
• LewisML -> Analisis Formasi Lewis
• MICA -> Komposisi kimia, dalam Shiny
• Analisis statistik multivariat dan pemodelan jaringan deviasi yang dipesan lebih dahulu untuk deteksi anomali geokimia unsur tanah jarang
  • kertas
• Pemetaan prospektivitas unsur tanah jarang melalui analisis data geokimia -> Pemetaan prospektivitas unsur tanah jarang melalui analisis data geokimia
• QMineral Modeller -> Asisten virtual Kimia Mineral dari survei geologi Brasil
• Perubahan Sekuler Terjadinya Subduksi Selama Archean -> Arsip kode Zenodo
  • [makalah] https://www.researchgate.net/publication/380289934_Secular_Changes_in_the_Occurrence_of_Subduction_During_the_ArcheanPendekatan pembelajaran mesin untuk diskriminasi batuan beku dan endapan bijih dengan elemen jejak zirkon

Kriging

• DKNN: jaringan saraf kriging dalam untuk interpolasi geospasial yang dapat ditafsirkan
  • kertas

Pemrosesan Bahasa Alami

• Ekstraksi Teks -> Ekstraksi teks dari dokumen : ML berbayar sebagai layanan, tetapi berfungsi dengan sangat baik, dapat mengekstrak tabel secara efisien
  • Skala Besar -> Versi skala besar
• Penandaan Konsep NASA -> Prediksi kata kunci
  • API -> layanan web API
  • Presentasi
• Ekstraktor Data Laporan Petrografi
• Pemodelan Topik Eksplorasi SA -> Pemodelan topik dari laporan eksplorasi
• Stratigraf
• Geokorpus
• BERT Portugis
• BERT CWS
  • kertas
• Ekstraksi Otomatis Hasil Lubang Bor Perusahaan Tambang
  • Makalah Konferensi

Penyematan Kata

• Model Bahasa Geosains -> pipa kode pemrosesan dan model [Glove, BERT) dilatih ulang pada dokumen geosains dari Kanada
  • Kumpulan Data -> Data untuk mendukung model
  • makalah -> Model bahasa geosains dan evaluasi intrinsiknya
  • makalah -> Penerapan Pemrosesan Bahasa Alami pada Data Teks Geosains dan Pemodelan Prospektivitas
• GeoVec -> Model penyematan kata yang dilatih pada 300 ribu makalah geosains
  • Model GeoVec -> Penyimpanan OSF untuk model GeoVec
  • kertas
  • GeoVecto Litho -> Interpolasi Model 3D dari penyematan kata
  • GeoVEC Playground -> Bekerja dengan model penyematan kata sarung tangan GeoVec Padarian
• GloVe -> Perpustakaan Standford untuk memproduksi penyematan kata
  • glove python glove, glove-python sangat bermasalah di windows: di sini versi biner untuk instalasi Windows:
• Mittens -> Dalam implementasi sarung tangan vektorisasi memori
• PetroVec -> Penyematan Kata Portugis untuk Industri Minyak dan Gas: pengembangan dan evaluasi
• wordembeddingsOG -> penyematan kata Minyak dan Gas Portugis
• Penyematan Kata Portugis
  • Model Portugis
• Penyematan Kata Spanyol
• Penyelarasan multibahasa
  • Ikhtisar pendekatan

Pengakuan Entitas Bernama

• Model Geo NER -> Pengenalan entitas bernama
• GeoBERT - memeluk repo wajah untuk model di
  • [kertas]https://www.researchgate.net/publication/359186219_Few-shot_learning_for_name_entity_recognition_in_geological_text_based_on_GeoBERT
• INDUS -> Paket LLM yang dirancang khusus untuk sains NASA
  • kertas
• Cara menemukan istilah kunci geosains dalam teks tanpa menguasai NLP menggunakan Amazon Comprehend
• OzRock - OzRock: Kumpulan data berlabel untuk pengenalan entitas dalam domain geologi (eksplorasi mineral).

Ontologi

• GAKG -> Grafik Pengetahuan Akademik Geosains Multimodal (Cina)
• GeoERE-Net -> Memahami laporan geologi berdasarkan grafik pengetahuan menggunakan pendekatan deep learning
  • kertas
• Ontologi GeoFault
• geosim -> Simulasi Kualitatif Proses Geologi yang Dipicu Secara Semantik
  • [https://www.duo.uio.no/handle/10852/111467](Pemodelan Pengetahuan untuk Geologi Digital) -> Tesis PhD dengan dua makalah
  • SIRIUS GeoAnnotator -> Contoh situs web dari atas
• Ontologi CWS
• Grafik Pengetahuan Stratigrafi (StraKG)
  • kertas

Model Bahasa Besar

• Model Bahasa Besar untuk Geosains
  • Makalah Model Bahasa Landasan Pembelajaran untuk Pemahaman dan Pemanfaatan Pengetahuan Geosains
• GeoGalactica -> Model bahasa dasar yang lebih besar dalam Geosains
  • laporan teknis
• GeoChat -> Model Bahasa Visi Besar yang membumi untuk Penginderaan Jauh
• LAGDAL -> LLM Mencocokkan informasi peta geologi dengan lokasi percobaan

bot obrolan

• GeoGPT -> Deep Time Digital Earth Research Group dari proyek Tiongkok

Penginderaan Jauh

• CNN Sentinel -> Ikhtisar klasifikasi penggunaan lahan dari data satelit dengan CNN berdasarkan kumpulan data terbuka
• Buku catatan DEA -> Contoh pembelajaran mesin yang dapat diskalakan tetapi banyak hal berguna di sini
• Buku catatan buku masak EASI -> Pengenalan platform CSIRO Earth Analytics untuk analisis gaya ODC
• DS_UNet -> Unet menggabungkan Sentinel-1 Synthetic Aperture Radar (SAR) dan Sentinel-2 Multispectral Imager
• Autoencoder Bertopeng Multi Dalih (MP-MAE)
• data
• segment-geospatial -> Segmentasikan apa pun untuk penggunaan geospasial
• SamGIS -> Segment Apa pun yang diterapkan pada GIS
• SatMAE++ -> Memikirkan Kembali Pra-pelatihan Transformers untuk Citra Satelit Multi-Spektral
• grid-mae -> Selidiki menggunakan grid multiskala dalam Vision Transformer Masked Autoencoder
• SkalaMae
  • kertas
• CIMAE -> CIMAE - Autoencoder Bertopeng Independen Saluran
• fork -> untuk memberinya nama sebagai referensi
• [Pembelajaran Representasi yang Diawasi Sendiri untuk Penginderaan Jauh] -> Tesis master mencakup hal di atas dan perbandingan beberapa model
• kamu gudang
  • kertas
• jaring tanah
• GeoTorchAI -> GeoTorchAI: Kerangka Pembelajaran Mendalam Spatiotemporal
• [pytorcheo](https://github.com/earthpulse/pytorchEO -> Pembelajaran Mendalam untuk aplikasi dan penelitian Observasi Bumi
• AiTLAS -> rangkaian benchmark sumber terbuka untuk mengevaluasi pendekatan pembelajaran mendalam yang canggih untuk klasifikasi gambar di Observasi Bumi
• Segmentasi Gym -> Gym dirancang untuk menjadi "toko serba ada" untuk segmentasi gambar pada "ND" - sejumlah pita yang bertepatan dalam gambar multispektral
• deep_learning_alteration_zones
• koleksi rasio pita penambangan yang mengagumkan -> kumpulan penggunaan rasio pita sederhana untuk menyorot berbagai mineral
• model dasar penginderaan jauh yang mengagumkan
• Clay -> Model dan antarmuka AI sumber terbuka untuk Earth
• Prithvi IBM-NASA-GEOSPAIAL
• Segmentasi gambar berdasarkan penyempurnaan model fondasi -> Untuk Prithvi
• AM-RADIO: Model Landasan Visi Agglomeratif
  • kertas -> - Kurangi Semua Domain Menjadi Satu
• RemoteCLIP -> Model Landasan Bahasa Visi untuk Penginderaan Jauh
• SpektralGPT
• zenodo) -> model dasar penginderaan jauh yang disesuaikan untuk data spektral
  • kertas -> Tak terlihat

Pengolahan

• Konversi ASTER -> Konversi dari ASTER hd5 ke geotiff NASA github
• Sumber Daya Data HLS -> Perselisihan Landsat Sentinel yang Harmonis
• sarsen -> pemrosesan dan koreksi gambar SAR berbasis xarray
• openEO -> openEO mengembangkan API terbuka untuk menghubungkan R, Python, JavaScript, dan klien lain ke back-end cloud EO

Pencampuran Spektral

• Survei-Klasifikasi-Gambar-Konvensional-ke-Transformator-untuk-Hiperspektral-2024
  • kertas
• Tinjauan Pembelajaran Mendalam Hiperspektral
• Autoencoder Hiperspektral
• Pelajari lebih dalam HSI
• Klasifikasi 3DCAE-hiperspektral
• DeHIC
• Rev-Net
  • kertas -> Jaringan Generatif Reversibel untuk Pencampuran Hiperspektral Dengan Variabilitas Spektral
• Pysptools -> juga memiliki algoritma heuristik yang berguna
• Spektral Python
• Kumpulan Data Spektral RockSL -> Buka kumpulan data spektral
• Tidak tercampur

Hiperspektral

• CasFormer: Cascaded Transformers untuk Pencitraan Hiperspektral Komputasi yang sadar akan Fusion
• Normalisasi Spektral untuk Keras
  • kertas
• S^2HM^2 -> S2HM2: Kerangka Pemodelan Terselubung Hierarki Spektral-Spasial untuk Pembelajaran Fitur yang Diawasi Sendiri dan Klasifikasi Gambar Hiperspektral Skala Besar
  • kertas

Visualisasi

• Ekstraksi Peta Warna Dalam dari Visualisasi
  • kertas
• Segmentasi Semantik untuk Mengekstraksi Gangguan Penambangan Permukaan Bersejarah dari Peta Topografi -> Contohnya adalah untuk tambang batu bara
  • kertas
• Kode Warna Kronostratigrafi Internasional -> Kode RGB dan lainnya dalam spreadsheet dan format lainnya
• LithClass -> kode warna litologi versi USGS
• versi warna
• SeisWiz -> Penampil python SEG-Y yang ringan

Tekstur

• Klasifikasi Tekstur Mineral Menggunakan Jaringan Neural Konvolusional Dalam: Penerapan Zirkon Dari Deposit Tembaga Porfiri
  • kertas

Simulasi

• Intelligent Prospector -> Perencanaan akuisisi data berurutan
• Zenodo
  • kertas

Geometri

• Sudut Dalam -> Perhitungan cepat sudut kontak pada gambar tomografi menggunakan pembelajaran mendalam

Lainnya

• Analisis Jaringan Sistem Mineralogi
  • Data -> Data dari kertas di sini
• Geoanalitik dan pembelajaran mesin
• Pembelajaran Mesin Bawah Permukaan
• ML Geosains
• Jadilah Detektif Geosains
• Earth ML -> Beberapa tutorial dasar dalam pendekatan PyData
• GeoMLA -> Algoritme Pembelajaran Mesin untuk data spasial dan spatiotemporal

Platform

Panduan

• CLI Geospasial - Daftar alat baris perintah geospasial
• Pembelajaran Mendalam Citra Satelit
• Pengamatan Bumi
• Kecerdasan Buatan Bumi
• GIS Sumber Terbuka -> Ikhtisar ekosistem yang komprehensif

Kualitas Data

• Kualitas Data Geosains untuk Pembelajaran Mesin -> Kualitas Data Geosains untuk Pembelajaran Mesin
• Data Gravitasi Australia -> Ikhtisar dan analisis data stasiun gravitasi
• Geodiff -> Perbandingan data vektor
• Redflag -> Analisis data dan gambaran umum untuk mendeteksi masalah

Pembelajaran Mesin

• Dask-ml -> Versi terdistribusi dari beberapa algoritma ML umum
  • kertas
• geospasial-rf -> Fungsi dan pembungkus untuk membantu aplikasi hutan acak dalam konteks spasial
• Geospatial-ml -> Instal beberapa paket umum sekaligus

Ruang Laten

• Fusi Bersarang
  • makalah -> Penggabungan Bersarang: Reduksi Dimensi dan Analisis Struktur Laten Data Bersarang Multi-Skala untuk Data M2020 PIXL RBU dan XRF

Metrik

• skor -> Memverifikasi dan mengevaluasi model dan prediksi dengan xarray

Probabilistik

• NG Boost -> regresi probabilistik
• ML Probabilistik
• Bagging PU dengan BO -> Bagging Positif Tanpa Label dengan Optimasi Bayesian

Kekelompokan

Peta Pengorganisasian Mandiri

• GisSOM -> Peta Pengorganisasian Mandiri yang berpusat pada geospasial dari Survei Geologi Finlandia
• SimpSOM -> Peta Pengorganisasian Mandiri

Lainnya

• hdbscan
• kmedoids
  • kmedoids
• Picasso

Bayesian

• Bayseg -> Segmentasi spasial

Penjelasan

• InterpretML -> Menafsirkan model data tabular
• InterpretML -> Penambahan komunitas

Pembelajaran Mendalam

• Ekstraksi Peta Warna Dalam -> Mencoba mengekstrak skala data dari gambar
• Ekstrak dan Klasifikasi Gambar dari Dokumen Geosains

Data

• Xbatcher -> Pembacaan data berbasis Xarray untuk pembelajaran mendalam
  • Pemuat data asli cloud untuk pembelajaran mesin menggunakan Zarr dan Xarray
• zen3geo -> Ilmu data gaya Xbatcher dengan pytorch

Penjelasan

• Nilai Bentuk
• Weight Watcher -> Analisis seberapa baik jaringan dilatih
• pengamat berat badan.ai
• Weightwatcher-ai.com -> Versi web profesional

Pembelajaran dengan pengawasan mandiri

• Diawasi Sendiri -> Implementasi kilat Pytorch dari beberapa algoritma
• Simclr
• Pembelajaran mandiri yang luar biasa -> Daftar yang dikurasi

Hyperparameter

• hiperopt
• TPOT Otomatis ML

Lingkungan Pengkodean

• Kotak Pasir DEA
• Kubus Dalam Kotak

Masyarakat

• Software Underground - Komunitas orang-orang yang tertarik menjelajahi persimpangan bawah permukaan dan kode
  • Pendaftaran Obrolan - Pendaftaran obrolan komunitas SWUNG
  • Yang paling penting- Layanan obrolan komunitas
    • Saluran Slack Lama (tidak digunakan lagi, lihat yang paling penting di atas)
  • Ikatan Sumber Terbuka Geosains
  • Video
    • Transformasi 2022
  • Geosains Terbuka yang Luar Biasa[catatan Bias Minyak dan Gas]
  • Contoh Peretasan Transformasi 2021
  • Tutorial Segisak 2021
  • Buku Catatan Seismik T21
  • Seismik Praktis dengan Python
  • Transformasi Simpeg 2021
• Pangeo
  • Forum
  • Praktik Terbaik COG
• Bumi Digital Australia
  • Saluran Kendur
• Yayasan Geospasial Sumber Terbuka
• OSGeoLive -> DVD/USB yang dapat di-boot dengan banyak perangkat lunak geospasial sumber terbuka
• ASEG -> video dari Perkumpulan Ahli Geosains Eksplorasi Australia
• AI untuk Pemodelan dan Pemetaan Geologi -> video dari hari konferensi
• konferensi

Penyedia Cloud

AWS

• ec2 Spot Labs -> Membuat instance Spot yang bekerja secara otomatis menjadi lebih mudah
• Sagemaker Geospasial ML
• Sagemaker -> Layanan Terkelola ML
  • SDK
  • Utilitas Titik Masuk
  • Lokakarya 101
  • Perangkat Pelatihan

Kelompok

• Shepard -> Pengaturan pembentukan cloud otomatis dari AWS Batch Pipelines: ini bagus

Paket

• Mlmax - Mulai perpustakaan cepat
• Masalah kecil
• Pyutil

Umum

• Wadah Pembelajaran Mendalam
• Loguru -> Perpustakaan logging
• Robot AWS GDAL -> Lambda dan pemrosesan geotiff secara batch
• Pemrosesan Seismik Tanpa Server
• LIthops -> kerangka komputasi terdistribusi multi-cloud

Ikhtisar

• Eksplorasi Mineral

Domain

• Geologi
  • Zaman Geologi
  • Litologi
  • Stratigrafi
• Geokimia
• Geofisika
• Penginderaan Jauh

Layanan Web

Kalau dicantumkan diasumsikan umumnya data, kalau hanya gambar seperti WMS akan tertulis begitu.

Dunia

• Mineral dan Deposit Kritis

Australia

• AusGIN
• Geosains Australia
• Potensi Mineral -> WMS
• Layanan Katalog Geoscience Australia

Geologi

• AUSLAMP -> Tennant Creek - MtIsa
• Geologi Lapangan
• Litosfer Dalam -> Potensi Mineral Litosfer Dalam
• Geokronologi -> Geokronologi
• Provinsi Geologi
• WMS -> Gambar WMS
• TELUR -> Perkiraan Permukaan Geologi dan Geofisika
• Batuan Alkali Proterozoikum - Kumpulan Data Batuan Alkali Proterozoikum WFS {juga memiliki WMS}
• Kenozoikum
• Mesozoikum
• Paleozoikum
• kuno
• Stratigrafi -> Satuan Stratigrafi

Geofisika

• Survei Geofisika
• Survei Seismik -> Survei seismik darat
• Magnetotelurik -> Stasiun AUSLAMP Australia Utara

Lainnya

• Ni-Cu-PEGE -> Intrusi menghosting Deposit PGE Tembaga Nikel
• Area EFTF -> Menjelajahi area masa depan
• Suhu -> Suhu yang ditafsirkan
• DEA -> Bumi Digital Australia
• Tutupan Lahan
• Perairan
• BOM -> Biro Meteorologi Hidrogeokimia

New South Wales

• negara bagian baru
• toilet
• Lubang Bor Mineral WFS
• Lubang Bor Minyak WFS
• Lubang Bor Batubara WFS
• Seismik -> Seismik dan lain-lain

Queensland

• Queensland
• Geosains -> Geofisika dan Indeks Laporan
• Geologi
• Daerah
• Negara
• Rumah petak
• Jalan
• Anak sungai

Australia Selatan

• SARIG
• lubang bor
• Geologi
• Geofisika
• Prospektivitas
• Mineral dan Tambang
• Penginderaan Jauh
• Seismik
• Rumah petak

Wilayah Utara

• NTGS -> Survei Geologi Wilayah Utara

Tasmania

• WFS Tasmania
• Istirahat Tasmania
• lubang bor

Victoria

• Victoria Geonetwork

Australia Barat

• Australia Barat
• Istirahat

Selandia Baru

• GNS -> Daftar layanan web

Amerika Selatan

Brazil

• Geoportal Brasil
• CPRM Brasil

Peru

• Penerimaan
• Keberadaan Mineral
• Lingkungan

Meksiko

• GeoInfo -> Layanan istirahat

Argentina

• SIGAM

Kolumbia

• Istirahat
• Setoran 2018

Uruguay

• Istirahat

Lainnya

• SIG Andes -> Geologi Andes

Eropa

EGDI -> Mineral EGDI

Swedia

• SGU Magnetik WMS
• SGU Uranium
• Metadata Geofisika

Finlandia

• GTK -> Survei Geologi Finlandia
  • Finlandia
    • Geologi Batuan Dasar
    • Geofisika
    • Survei Darat
• Mineral Arktik -> Kemunculan Mineral Arktik 1 Juta

Denmark

• deus -> Greenland WMS/WFS

Portugal

• Geologi Portugal
• Kemunculan Mineral -> WMS
• Kota dan Kota

Spanyol

• Spanyol
• Geologi -> 200K
• 1M -> 1M
• 50K -> 50K
• IGME Geode
• Geofisika
• Tembaga - Tembaga
• GeoFPI -> Geologi dan Mineral Zon Portugis Selatan
• Air

Ukraina

• Geoinform -> [saat ini ditangguhkan]

Irlandia

• Istirahat
• Lokasi Mineral

Britania

• BGS -> Survei Geologi Inggris
• Geoindex -> contoh kejadian mineral
• Istirahat -> Layanan Istirahat BGS & Inspirasi 625

Jerman

• BRG

Republik Ceko

• Istirahat

Slowakia

• Istirahat

Hongaria

• MBFZ OGC
• Istirahat MBFZ

Rumania

• IGR -> WMS saja
• Minres IGR -> WMS saja

Polandia

• Contoh lainnya -> Lebih banyak server peta

Amerika Utara

Kanada

• Quebec
• NWT
• Istirahat
• Referensi

Amerika Serikat

• Mineral Dunia USGS
• USGS MRDS
• Minnesota

Asia

• Cina -> wap deposit mineral WMS
• ladang bijih -> Titik kemunculan mineral
• Mineral India -> WMS
• Geofisika India

Afrika

• Geoportal Afrika -> Layanan istirahat
• Afrika 10 juta -> Kemunculan Mineral Afrika 10 juta https://pubs.usgs.gov/of/2005/1294/e/OF05-1294-E.pdf
• Tambang Artisanal IPIS -> Ada versi WMS juga
• github
• Uganda -> GMIS WMS

Umum

• Layanan Web Eksplorasi Mineral -> Plugin QGIS dengan akses ke banyak layanan web yang relevan

Lainnya

• Buka Peta Jalan -> layanan ubin umum yang berguna

Lebah

• Buka API Data -> API Portal Data Terbuka GSQ
  • Penguraian geokimia
• INTI -> Buka Teks Penelitian
  • Notebook API -> Contoh dan fungsinya
• BERBAGI -> Buka API Sains
• Publikasi USGS
• REF SILANG
• xDD -> mantan GeoDeepDive
• ADEPT -> GUI ke xDD untuk mencari 15 juta kertas yang dipanen
• BukaAlex
  • api
  • Perpustakaan Python diophila
  • Perpustakaan Python
    • kertas
• Makrostrat
  • kertas
• OpenMinData -> memfasilitasi kueri dan pengambilan data mineral dan geomaterial dari API Mindat

Portal Data

Dunia

• Kolaborasi Model Bumi -> akses ke berbagai model Bumi, alat visualisasi untuk pratinjau model, fasilitas untuk mengekstrak data/metadata model dan akses ke perangkat lunak dan skrip pemrosesan yang dikontribusikan.
• Buletin ISC -> Pencarian mekanisme fokus gempa
• [Konsorsium Informasi Magnetik[(https://www2.earthref.org/MagIC/search) -> paleomagnetik, geomagnetik, batuan magnetis

Australia

Geosains Australia

• Katalog Data Geoscience Australia
• AusAEM
• Portal Geosains Australia
• Menjelajahi Portal Masa Depan -> Portal web Geoscience Australia dengan informasi unduhan
• AusAEM
• AusLAMP
• Geokronologi dan Isotop
• Daerah Tangkapan Hidrogeologi -> mencari lapisan daerah tangkapan air
• Inisiatif Pemetaan Mineral Kritis
• Unit Stratigrafi Australia
• Unit Stratigrafi Lubang Bor Australia -> Kompilasi air tanah dari unit sedimen
• Geoscience Australia Geofisika thredds -> OpendDAP dan akses https
• MORPH gdb -> Data pengeboran Petugas Musgrave

CSIRO

• Portal Akses Data CSIRO
• Kedalaman Regolit
• TWI -> Indeks Kebasahan Topografi
• Peta Geosains ASTER -> Situs Web
  • FTP -> situs ftp CSIRO
  • Catatan ASTER Maps -> Catatan untuk hal di atas

AuScope

• Geologi 3D -> Model dari berbagai area

TIGA BARANG

• Kovariat Bare Earth yang Ditingkatkan untuk Pemodelan Tanah dan Litologi

Biro Meteorologi

• Penjelajah Air Tanah -> Biro Meteorologi

Data Spasial Dasar

• Elvis

Australia Selatan

• SARIG -> Pencarian berbasis peta geospasial Survei Geologi Australia Selatan
• Katalog SARIG -> katalog data
  • Model 3D
    • SA Lebar
    • lokasi s3
  • Paket Data - Pembaruan tahunan
  • s3 Reports -> Laporan dan versi teks di bucket s3 dengan antarmuka web)
  • Laporan
  • Seismik
    • Unduhan seismik -> Satu halaman tautan

Wilayah Utara

• STRIKE -> Survei Geologi Wilayah Utara
• PERMATA
  • Cekungan McArthur -> Model 3D
  • Survei Geofisika
  • Geofisika -> referensi
  • Pengeboran dan Geokimia -> referensi
    • Paket Data -> paket data

Queensland

• Survei Geologi Queensland
• Survei Geofisika
• Pengeboran dan geokimia

Australia Barat

• GEOVIEW -> Survei Geologi Australia Barat
• DMIRS -> Pusat Data dan Perangkat Lunak DMIRS
• URL Unduhan -> kumpulan data tautan unduhan
• Pengeboran dan Geokimia
• Unduh paket - peningkatan?
• Geokimia
• Sumur Minyak dengan kedalaman
• subset data WA

negara bagian baru

• MINVIEW -> Survei Geologi New South Wales
• DiGS -> Publikasi dan koleksi Geoteknik

Tasmania

• MRT
• Peta MRT -> Peta Web

Victoria

• Sumber Daya Bumi
  • Geofisika
• GeoVIC -> Webmaps memerlukan registrasi agar lebih berguna

Selandia Baru

• Basis Data Eksplorasi -> Online
• GERM -> Peta Sumber Daya Geologi Selandia Baru
• Geologi -> Peta Web
  • https://maps.gns.cri.nz/gns/wfs

Amerika Selatan

Brazil

• CPRM -> Survei Geologi Brasil
• Unduhan -> Unduhan Survei Geologi Brasil
• Rigeo -> Repositori Institusional Geosains

Peru

• Ingemmet GeoPROMINE -> Survei Geologi Peru
• GeoMAPE

Meksiko

• Info Geo

Argentina

• SIGAM -> Survei Geologi Argentina
• SIGAM

Kolumbia

• Geportal

Uruguay

• gambar

Chili

• Portalgeomin

Eropa

• EGDI -> Geosains Eropa
• WFS
• Promin
• Menginspirasi -> Menginspirasi Geoportal

Denmark

• Data Bawah Permukaan Denmark

Finlandia

• Mineral4EU
• GTK -> Survei Geologi Finlandia
• Peta Geokimia -> pdf saja!

Swedia

• SGU -> Survei Geologi Swedia

Spanyol

• IGME -> Survei Geologi Spanyol

Portugal

• Portal Geo
• Keberadaan Mineral

Irlandia

• GSI -> Survei Geologi Irlandia
• GSI - Penampil peta
• Tambang Emas -> Pencarian peta dan dokumen
• data.gov.ie -> Tampilan portal nasional
• isde -> Pertukaran Data Spasial Irlandia

Norwegia

• NGU -> Survei Geologi Norwegia
• database -> Pencarian sumber daya mineral dan stratigrafi
• github
• API
• Geoporta -> Geofisika
• GEONORGE -> Katalog data dengan unduhan

Britania

• Britania
• server peta
• github

Ukraina

• Sumber Daya Mineral

Rusia

• Institut Penelitian Geologi Rusia -> Saat ini tidak dapat diakses
• RGU -> Proyek simpanan GIS

Jerman

• Portal Geo
• Peta Geo -> M
• Atom -> Umpan data atom
• GDI -> Model 3D Jerman

Perancis

• Infoterre -> Survei Geologi Prancis

Kroasia

• Geoportal -> Survei Geologi Kroasia
• Geologi

Republik Ceko

• GS -> Survei Geologi Ceko

Slovenia

• Katalog Data

Slowakia

• Katalog Data
• Api geoportasi

Hongaria

• Peta MBFSZ

Rumania

• IGR -> Survei Geologi Rumania
• Sumber Daya Mineral

Polandia

• Portal Geo

Inggris Raya

• Perpustakaan Geofisika Darat Inggris
• OS Data Hub Geologi Inggris
• Geologi 625

Amerika Utara

Kanada

• Sumber Daya Alam Kanada
• github
• Repositori Data Geosains -> Server DAP
• Portal Peta Web Penambangan
• DEM -> DEM Kanada dalam format COG
• CDEM -> Model Ketinggian Digital (2011)
• Ontario
• Quebec
• Pangkalan Data SIGEOM
• British Columbia
• Basis data keberadaan mineral
• Yukon
  • ftp
• Nova Scotia
• provinsi
• Pulau Pangeran Edward
• Saskatchewan
• Basis data kejadian mineral
• Newfoundland -> tidak berfungsi di Chrome, mencobanya di Edge
• Alberta
• Aplikasi Pemetaan Interaktif
• Wilayah Barat Laut
• Kepemilikan Mineral

Amerika Serikat

• USGS -> Basis data peta
• MRDS -> Sistem Data Sumber Daya Mineral
• Earth Explorer -> Portal Data Penginderaan Jauh USGS
• Basis Data Peta Nasional
• Basis Data Peta Nasional
• Alaska
• ReSci -> Pendaftaran Koleksi Ilmiah Program Pelestarian Data Geologi dan Geofisika Nasional
• Michigan

Afrika

• Kadaster
• Hidrogeologi -> Hidrogeologi dan geologi dari atlas air tanah
• Afrika Barat -> Deposit mineral
• Namibia
• Keberadaan Mineral
• Penambang
• Afrika Selatan -> survei geologi Afrika Selatan
• Kemunculan Mineral -> Contoh di mana Anda harus masuk untuk mengunduh
• Uganda -> Portal GMIS
• Mineral logam
• Tanzania
• Keberadaan Mineral
• tambang
• SIGM -> Geologi dan Pertambangan Tunisia
• Zambia -> Rumah petak Zambia di sini

Asia

Cina

• Data Geosains
• Keberadaan Mineral
• Database Deposit Mineral Nasional

India

• Bhukosh -> Survei Geologi India
• Catatan Geologi Rajasthan tidak berfungsi kecuali sedikit demi sedikit yang menyakitkan - jika Anda menginginkannya, beri tahu saya

Arab Saudi

• Portal Basis Data Geologi Nasional

Lainnya

Geologi

• StratDB
• Kesalahan Aktif Global GEM
• Sifat Mineral RRuff
• artikel -> Sistem evolusi mineralogi
• Satu Geologi
• katalog

Iran

Geologi

• Batuan Dasar OGC BGS 1M

Umum

• OSF -> Yayasan Sains Terbuka
• Logam Dasar yang Ditampung Sedimen -> Logam Dasar yang Ditampung Sedimen
• Batas Atenosfer Litosfer -> LAB Hoggard/Czarnota
• Daftar Survei Geologi

Laporan

Australia

• GEMIS Wilayah Utara
  • mencari
  • contoh
• SARIG Australia Selatan
• WAMEX Australia Barat
  • mencari
  • api
    • contoh
• Queensland
• Penggalian NSW
  • NSW Penggalian terbuka
  • API tidak bersifat publik
• PorterGEO -> Database deposit mineral dunia dengan ringkasan ikhtisar
• Institut Mineral Berkelanjutan -> Organisasi peneliti yang berafiliasi dengan universitas di Queensland yang memproduksi kumpulan data dan pengetahuan

Kanada

• British Columbia
  • Pencarian -> Laporan Penilaian Mineral
  • Publikasi -> Publikasi
• Ontario -> Laporan Penilaian Mineral
  • Publikasi Ontario
• Alberta
  • Publikasi
• Yukon
• Tapak
• Manitoba
• Publikasi
• Newfoundland dan Labrador
• Wilayah Barat Laut
• Nova Scotia
• Quebec
• Saskatchewan
  • Mencari
  • iMaQs -> Sistem Penambangan dan Penggalian Terintegrasi

Amerika Serikat

• Arizona
• montana
• Nevada
• Meksiko Baru
• Minnesota
• Michigan
• json
• Alaska
• Washington

Lainnya

• Survei Geologi Inggris NERC
  • Potensi Mineral
  • Mencari
  • Contoh API
  • Publikasi
  • MEIGA -> Laporan proyek eksplorasi mineral MEIGA 600 BGS
• GeoLagret -> Swedia
• MinData -> Kompilasi lokasi batuan dari seluruh dunia
• Mineral Databse -> Daftar mineral yang dapat diekspor dengan sifat dan usia ilmiah
• NASA
• ResearchGate -> Peneliti dan Jaringan Profesional

Peralatan

GIS

• QGIS -> Visualisasi Data GIS dan Analisis Aplikasi Desktop Sumber Terbuka, memiliki beberapa alat ML: sangat diperlukan untuk beberapa tampilan yang cepat dan mudah
  • Geologi 2D di QGIS -> Lokakarya untuk QGIS NA 2020 Memperkenalkan peta geologis dan penampang untuk siswa dan penggemar
  • OpenLog -> Beta plugin bor
  • Geo -Sam -> Plugin QGIS untuk segmen apa pun dengan raster
  • Bobot-of-Evidence
  • plugin
• RUMPUT
• saga -> cermin dari sourceForge

3D

• Pyvista -> VTK Wrapping API untuk visualisasi dan analisis data yang hebat

  • Pvgeo
  • Pyvista -xarray -> mengubah data xarray ke vtk 3d tanpa rasa sakit: perpustakaan yang hebat!
  • Omfvista -> Pyvista untuk format penambangan terbuka
  • Tutorial Scipy 2022

• Pymeshlab -> transformasi mesh

• Buka format penambangan

• Alat Whitebox

  • GUI -> Versi Desktop

• Di bawah permukaan

• Geolambda -> Pengaturan AWS Lambda

• Analis Geoscience

  • geoh5py -> mendapatkan data ke dan dari proyek geoh5
  • GeoApps -> Aplikasi Berbasis Notebook untuk Geofisika melalui Geoh5py
  • geoh5vista
  • Gams -> Analisis Data Magnetik
    • Kertas - Kerangka Kerja untuk Data Geoscience Mineral dan Model Portabilitas - GeOH5

• Rayshader

• Vdeo

Jenderal Geospasial

• Sumber Daya Python untuk Ilmu Bumi
• Geoutils -> Analisis Geospasial dan Foster Inter -Operability antara paket GIS Python lainnya.

Data vektor

ular piton

• Geopandas
  • Dask-geopandas
    • tutorial
  • Geofileops -> Peningkatan Kecepatan Bergabung melalui Fungsi Database dan Geopackage
• Kart -> Kontrol Versi Terdistribusi untuk Daata
• Pyesridump -> perpustakaan untuk mengambil data pada skala dari server ESRI REST

R

• SF
• Terra -> Terra menyediakan metode untuk memanipulasi data geografis (spasial) dalam bentuk "raster" dan "vektor".

Data raster

C

• ExactExtract -> Command Line Zonal Stats di C

Julia

• Rasters.jl -> Membaca dan Menulis Jenis Data Raster Umum

ular piton

• Rasterio -> pustaka base python untuk penanganan data raster
• Georeader -> Proses data raster dari berbagai misi satelit
• Rasterstats -> meringkas dataset raster geospasial berdasarkan geometri vektor
• Xarray -> Penanganan dan Analisis Array Berlabel Multidimensi
  • RioxArray -> API tingkat tinggi yang luar biasa untuk penanganan Xarray data raster
  • Geocube -> Rasterisation of Vector Data API
  • ODC -geo -> Alat untuk penanganan raster berbasis penginderaan jauh dengan banyak alat yang sangat berguna seperti warna, alur kerja grid
  • Validator COG -> Memeriksa format geoTIFF yang dioptimalkan cloud
  • Serverless-Datacube-Demo-> Xarray via Lithops / Coiled / Modal
  • Xarray Spatial -> Analisis Statistik Data Raster Seperti Klasifikasi Seperti Istirahat Alami
  • xdggs -> jenis kisi lainnya
  • xgcm -> histogram dengan label
  • xrft -> transformasi Fourier berbasis xarray
  • XVEC -> Kubus Data Vektor untuk Xarray
  • xarray -einstats -> statistik, aljabar linier dan einops untuk xarray

R

• Raster -> r Library
• Terra -> menyediakan metode untuk memanipulasi data geografis (spasial) dalam bentuk "raster" dan "vektor".
• Bintang -> Array Spatiotemporal: Raster dan Vektor Datacubes
• ExactExtracr -> Statistik Zonal Raster untuk R

Tolok ukur

• Raster -Benchmark -> Benchmarking Beberapa Libaries Raster di Python dan R

Gui

• Alat Whitebox -> Python API, GUI, dll. Telah digunakan untuk perhitungan indeks basah topografi

Pengumpulan data

• Piautostage-> 'Alat cetak 3D open-source untuk koleksi otomatis citra mikroskop resolusi tinggi;' Dirancang untuk sampel mineral.

Konversi Data

• AEM ke Seg-y
• ASEG GDF2
• CGG Outfile Reader
• Geosoft Grid to Raster
• Loop Geosoft Grid
• Harmonica Geosoft Grid -> Tarik Permintaan yang sedang berlangsung pada Konversi ke Xarray
• Auscope -> Data dari model Binary Gocad
• Pembaca Grid Gocad SG
  • Geomodel-2-3DWeb-> Di sini mereka memiliki metode untuk mengekstrak data dari grid Binary Gocad SG
• LEAPFROG MESH READER
• OMF -> Buka format penambangan untuk konversi antara berbagai hal
• Penambang PDF
• VTK ke DXF

Geokimia

• Pygeochemtools -> Perpustakaan dan baris perintah untuk mengaktifkan QC cepat dan plot data geokimia
• Peta geokimia SA -> Analisis Data dan Plot Data Geokimia Australia Selatan dari Survei Geologi SA
• Levenning Geokimia
• Tutorial Geokimia Scott Halley
• Tabel Berkala

Geostatistik

• Geostatspy
• Pyinterpolate

Geokronologi

• Skala Waktu Geologi -> Kode untuk diproduksi, tetapi juga memiliki CSV reguler yang bagus dari usia

Geologi

• GEMPY -> Pemodelan Implisit

• GEMGIS -> Bantuan Analisis Data Geospasial

• Loopstruktural -> Pemodelan Impsed

• Manual Python Geologia -> Analisis Data Geologi

• MAP2LOOP -> Otomasi Pemodelan 3D

  • Loop3d -> gui untuk map2loop

• Pybedforms

• SA Stratigraphy -> Webapp Editor Database Stratigrafi

• Striplog

• Analise_de_dados_estruturais_altamira

• Global Tectonics -> Open Source Dataset untuk dibangun, piring, margin dll.

  • kertas

• Penambahan Zenodo

• Litholog

• plat pyg

• Data tutorial

  • kertas

Geofisika

• Utilitas Geoscience Australia
• Geofisika untuk mempraktikkan geosains
• Potensi Toolbox Bidang Potensi -> Beberapa filter transformasi Fourier berbasis xarray - turunan, pseudogravity, rpg dll.
  • Notebook -> Kelas dengan beberapa contoh [turunan vertikal, pseudogravity, kelanjutan ke atas dll)
• Kotak pasir geofisika komputasi
• Ris Basement Sediment -> Depth to Magnetic Basement di Antartika
• Pemrosesan gambar sinyal

Elektromagnetik

• Geoscience Australia AEM
• UH Electromagnetics -> Cursework Notebook tentang Memahami Domain Ini
• Interpretasi AEM
• Emag Py -> fdem
• Resipy -> dc / ip

Gravitasi dan Magnetika

• Harmonika
• Contoh Filter -> Pemrosesan Berbasis Fourier Transformasi Cepat melalui Xarray
• Data Gravitasi Australia
• Cacing
• Pembaruan cacing <- Potensi bidang pembuatan cacing dengan beberapa pembaruan kecil untuk menangani NetworkX API baru *Github Mirror
• Osborne Magnetic -> Contoh Pemrosesan Data Survei

Seismik

• Segyio
• Segysak -> Penanganan dan Analisis Data Seg -Y Berbasis Xarray
• Catatan Geofisika -> Pemrosesan Data Seismik

Magnetotellurics

• Mtpy
• Tutorial
• Mtpy -> pembaruan di atas untuk membuat segalanya lebih mudah
• Mineral Stats Toolkit -> Jarak ke Analisis Fitur MT
• Kertas konduktor litosfer
• MTWAFFLE -> Contoh Analisis Data MT
• Pymt
• Resistis
• Mecmus -> Alat untuk Membaca Model Konduktivitas Listrik Amerika Serikat
• model

Kisi -kisi

• GMT
  • Pygmt
• Verde
• Grid_aeromag -> Contoh Gridding Brasil
• pyinterp -> gridding multidimensi melalui boost
• Pseudogravity -> dari blakely, 95

Inversi

• Simpeg
  • Mira Geoscience Fork -> Digunakan untuk GeoApps
  • Fork Simpeg
  • Transform 2020 Simpeg
  • Transformasi 2021 Simpeg
  • Skrip SIMPEG
  • Contoh inversi sendi astic
• Gimli
• Tomofast-x
• USGS Anonim FTP
• USGS Software -> Daftar yang lebih lama dari hal -hal yang lebih lama bermanfaat: Dosbox, siapa pun?
• Subrutin Geofisika -> Kode Fortran
• 2020 Masalah inversi aachen -> Tinjauan Teori Inversi Gravitasi

Geokimia

• Pirolit
  • GS2021 Pyrolite
• Penyamarataan
• Alat Pygeochem
• Geoquimica
• Geochemistrypi
  • kertas

Pengeboran

• Dh2loop -> Bantuan Interval Pengeboran
  • kertas
• DRILLDOWN -> Visualisasi Pengeboran di Notebook Melalui Geoh5py -> Catatan Desurveying
• Pygslib -> Survei Downhole dan Normalisasi Interval
• Pyborhole -> memproses dan memvisualisasikan data lubang bor
• DHCOMP -> Data Geofisika Komposit ke serangkaian interval

Penginderaan jauh

• Indeks spektral yang luar biasa -> panduan untuk pembuatan indeks spektral
• Buka Data Cube
  • DEA Notebooks -> Kode untuk digunakan dalam alur kerja gaya ODC
  • DataCube -Stats -> Perpustakaan Analisis Statistik untuk ODC
  • GEO NOTORBOOKS -> Contoh kode dari elemen 84
• Raster4ml -> sejumlah besar indeks vegetasi
• Lefa -> Analisis Fraktur, Lineaments

Serverless

• Kerchunk -> akses tanpa server ke data berbasis cloud melalui Zarr
  • Kerchunk Geoh5 -> Akses ke Analis Geoscient/Geoh5 Proyek tanpa server melalui Kerchunk
  • ICEHUNK -> Mesin penyimpanan transaksional untuk data tensor / nd -array yang dirancang untuk digunakan pada penyimpanan objek cloud.

Katalog STAC

• DEA Stackstac -> Contoh Bekerja dengan Data Digital Earth Australia
• Intake-stac
• Ekstensi ML AOI
• Spesifikasi Ekstensi Model ML -> Spesifikasi Model Pembelajaran Mesin untuk Model KatalogingSpatio -Temporal
  • kertas
• ODC -STAC -> Database gratis buka data kubus
• Pystac
• Sat-Search
• StackStac -> Metadata mempercepat waktu Dask dan Xarray

Statistik

• Orange -> Data Mining GUI
• HDStats -> Dasar Algoritmik Median Geometris
• Hdmedian

Visualisasi

• TV -> Lihat citra satelit di terminal
• Titiler
• Duduk
• Hsdar
• Bintang
• Peru Gold Mining Sar

Potensi mineral

• POTING POTENSI MINERAL Nikel -> Analisis Berbasis ESRI
• Alat online prospektif

Ekonomi Pertambangan

• BlueCap -> Kerangka kerja dari Universitas Monash untuk menilai kelayakan tambang
• Hukum ZIPFS ​​-> Kurva Memasangkan Distribusi Deposisi Mineral
• Pyasx -> pengikisan umpan data ASX
• API Harga Logam -> Microservice Containerized

Visualisasi

• Napari -> Penampil Gambar Multidimensi
• HOLOVIEWS -> Visualisasi Data Skala Besar
• GraphViz -> Info Instalasi Plotting/Melihat Bantuan Windows Windows
• Spatial-kde

Colormaps

• CET Colormaps Seragam Perseptual
• Pu colormaps -> diformat untuk pengguna di analis geoscience
• Distorsi Colormap -> Aplikasi Panel untuk Menunjukkan Distorsi yang Dibuat oleh Colormaps Non -Perseptual pada Data Geofisika
• Merobek data dari colormpas
• Buka Proyek Kode Geoscience

Geospasial

• Geospasial>- Memasang beberapa paket Python umum
• Python geospasial -> Daftar Kurator

Tumpukan Teknologi

C

• Gdal -> Kerangka kerja transformasi dan analisis data yang sangat penting
  • Alat -> Catatan memiliki banyak alat baris perintah yang sangat berguna juga

Julia

• Julia Earth -> Membina Ilmu Data Geospasial dan Pemodelan Geostatistik dalam Ilmu Bumi
• Julia Geodynamics -> Kode Geodinamika Komputasi
• Pengantar Julia untuk Geoscience

Python - Pydata

• Anaconda -> Dapatkan banyak yang sudah diinstal dengan manajer paket ini.
  • Gdal et al -> Keluarkan rasa sakit dari pemasangan Gdal dan TensorFlow di sini
  • Git Bash -> Mendapatkan Conda untuk bekerja di Git Bash
• Array multidimensi numpy
• Analisis Data Tabel Pandas
• Visualisasi Matplotlib
• Zarr -> array terdistribusi terkompresi dan terkompresi
• Dask -> Komputasi Paralel, Terdistribusi
  • Penyedia Cloud Dask -> Secara Otomatis Mulai Cluster Dask di Cloud
  • Dask Median -> Notebook Memberikan Prototipe Fungsi Median Dask
• Ekosistem Geospasial Python -> Informasi yang dikuratori

Rust - Georust

• Georust -> Koleksi utilitas geospasial dalam karat

Basis Data

• Duckdb -> dalam proses OLAP db dengan kecepatan - memiliki beberapa kemampuan geospasial dan array
• Ibis + DuckDB Geopsatial -> Scipy2024 Talk

Ilmu Data

• Templat Ilmu Data Python -> Pengaturan Paket Proyek
• Ilmu Data Python yang Luar Biasa -> Panduan Kurator

Kemungkinan

• Distfit -> Probability Density Fitting

Sains

• Sumber Daya Python untuk Ilmu Bumi
• Komputasi ilmiah yang luar biasa

Buruh pelabuhan

• AWS Deep Learning Containers
• Docker spasial
• DL Docker Geospatial
• Kursi goyang
• Docker Lambda
• Geobase
• DL Docker Geospatial

Ontologi

• Masyarakat Geologi Kosakata Queensland
  • Database Properti Geologi
  • Geofeatures
• Masyarakat Geologi Australia Barat
• Stratigrafi
• Manajer Pengetahuan Geoscience
• Kosakata Geosciml

Buku

ular piton

• Buku masak analisis geospasial Python
• Geoprocessing dengan Python -> Manning Livebook

Lainnya

• Buku pelajaran
• Pembelajaran mesin di industri minyak dan gas
• Geokomputasi dengan r
• Earthdata Cloud Cookbook -> Cara Mengakses Sumber Daya NASA
• Cookbook Data Cleaner -> Menempatkan alat Unix untuk digunakan dengan baik untuk perselisihan dan pembersihan data
• Encyclopedia of Mathematical Geosciences
• Buku Pegangan Geosains Matematika

Lainnya

• Gxpy -> Geosoft Python API
• Eartharxiv -> Unduh kertas dari arsip pracetak
• Essoar -> Preprint Paper Archive

Kumpulan data

Dunia

Geologi

• Bedrock -> Generalisasi Geologi Dunia
• GLIM -> Peta Litologi Global
• Paleogeologi Sebuah atlas peta paleogeografi phanerozoikum
• Lapisan sedimen -> Global 1 km ketebalan tanah, regolith, dan lapisan deposit sedimen
• World Stress Map -> Kompilasi Global Informasi tentang Bidang Stres Kerak Saat Ini
• GMBA -> Global Mountain Inventory

Geofisika

Gaya berat

• Kelengkungan -> Analisis kelengkungan global dari data gradien gravitasi
• WGM 2012

Kemaknitan

• EAMG2V3 _> Earth Magnetic Anomaly Grid
• WDMAM -> Peta Anomali Magnetik Digital Dunia

Magnetotellurics

• EMC -> Model Konduktivitas Listrik 3D Global 3D

Seismik

• Lab Slnaafsa
• Lab Cam2016
• Moho -> Data Gemma
• MOHO -> DATA SZWILLUS
• Kecepatan Seismik -> Debayle et al
• Lithoref18 -> Model referensi global litosfer dan mantel atas dari inversi gabungan dan analisis beberapa set data
• Crust1.0 -> Model Kerak Global NetCDF
• Ikhtisar Beranda

Panas

• Aliran panas

Umum

• Deep Time Digital Earth -> Data dan Visualisasi untuk berbagai sumber data dan model
• Earthchem -> Pelestarian, Penemuan, Akses, dan Visualisasi Data Geokimia, Geokronologis, dan Petrologi yang Digerakkan
• Georoc -> Komposisi Geokimia Batuan
• Geologi Global -> Resep singkat untuk membuat peta geologi global dalam format GIS (misalnya Shapefile), dengan rentang usia yang dipetakan ke skala waktu GTS2020
• Komisi Provinsi Igenus Besar
• Gumpalan mantel
• Ketebalan sedimen -> peta
• spatialreference.org -> repositori untuk situs web

Australia

• Model Bumi Umum
• Peta mineral berat
• Peta Mineral Berat Australia Pilot
• Aplikasi mengkilap

Geokimia

• Kisi -kisi prediktif konsentrasi oksida utama dalam batuan permukaan dan regolith di atas benua Australia -> berbagai oksida

Geologi

• Atlas Batu Alkali
• Kenozoikum
• Mesozoikum
• Paleozoikum
• Archaean
• mencari
• Batuan Alkali Proterozoikum -> Alkali Proterozoik
• Kenozoikum
• Mesozoikum
• Paleozoikum
• Archaean
  • kertas https://ecat.ga.gov.au/geonetwork/srv/eng/catalog.search#/metadata/147963
• Hidrogeologi -> Peta Hidrogeologi Australia
• Hidrogeologi -> 5m
• Geologi berlapis -> 1m
• Geologi Permukaan -> Skala 1M
• Australia Australia Mafik-Ultramafic Magmatik Acara GIS Dataset

Geofisika

• Gravity -> 2019 Grid Grid Nasional Australia 2019

Kemaknitan

• TMI -> Peta Anomali Magnetik Australia, Edisi Ketujuh, TMI 2019
• Versi 40m -> 40m
• VRTP -> Total Magnetic Intensity (TMI) Grid Australia dengan reduksi variabel ke Pole (VRTP) 2019
• 1VD -> Total Grid Intensitas Magnetik Australia 2019 - Derivatif Vertikal Pertama (1VD)

Radiometrik

• Radiometrik -> Radiometric Grid of Australia (RADMAP) V4 2019 dengan pemodelan yang dimodelkan
• K -> Radiometrik Grid of Australia (RADMAP) V4 2019 Filing Potasium PCT Difilter
• U -> Radiometrik Grid of Australia (RADMAP) V4 2019 Filter PPM Uranium
• Th -> Radiometric Grid of Australia (RADMAP) V4 2019 Filter PPM Thorium
• Th/K -> Radiometric Grid of Australia (RADMAP) V4 2019 Rasio Thorium over Potassium
• U/K -> Radiometric Grid of Australia (RADMAP) V4 2019 Rasio Uranium over Potassium
• U/th -> Radiometric Grid of Australia (RADMAP) V4 2019 Rasio Uranium over Thorium
• U Squared/Th -> Radiometric Grid of Australia (RADMAP) V4 2019 Rasio Uranium Kuadrat di atas Thorium
• Tingkat Dosis-> Radiometrik Grid of Australia (RADMAP) V4 2019 Laju Dosis Terestrial Disaring
• Gambaran Ternary -> Radiometrik Grid of Australia (Radmap) V4 2019 - Gambar Ternary (K, TH, U)

Ausaem

• Ausaem 1 -> Ausaem Tahun 1 NT/QLD Survei Elektromagnetik Udara; Produk Inversi Bumi Lapis Ga
• Ausaem 1 -> Ausaem Tahun 1 NT/QLD: Tempest® Data Elektromagnetik Udara dan Estimasi Konduktivitas EM Flow®
• Ausaem 1 -> Paket Data Interpretasi Ausaem1
• Ausaem 2 -> Ausaem 02 WA/NT 2019-20 Survei Elektromagnetik Udara
• Ausaem -wa -> ausaem -wa, blok survei elektromagnetik udara Murchison
• Ausaem-wa-> ausaem-wa, blok survei elektromagnetik udara barat daya barat daya
• Ausaem -wa -> Ausaem WA 2020-21, Goldfields Eastern & East Yilgarn Airborne
• Ausaem -Wa -> Ausaem (WA) 2020-21, strip Earaheedy & Desert
• Ausaem ERC -> Koridor Sumber Daya Timur Ausaem
• Ausaem WRC -> Koridor Sumber Daya Barat Ausaem
• Tinjauan Interp
• Permukaan Nasional dan Kotak Konduktivitas Nasional -Interpolasi ML Nasional untuk AUSEM dengan cara yang mirip dengan Australia Utara

Auslamp

• Auslamp Sea -> Model Resistivitas Dasar Daratan Australia Tenggara dari Data Auslamp Magnetotelluric
• Data Victoria
• Data NSW
• Auslamp Tisa -> model resistivitas yang berasal dari magnetotellurics: proyek auslamp -tisa
• Auslamp delamerian -> model resistivitas litosfer orogen Delamerian dari data magnetotelluric auslamp
• Auslamp ne sa
• Auslamp Gawler
• Stasiun Auslamp -> sekitar tahun 2017
• Kertas Tasmanides

Moho

• Moho 2019 dan 2023
  • kertas

Deposit Mineral

• Pengaturan geologis, usia dan endowmen deposit mineral utama Australia
• Dataset komprehensif untuk produksi tambang Australia 1799 hingga 2021
  • kertas

Potensi mineral

• Tinjauan Umum - Geoscience Australia -> Tinjauan Umum Publikasi dan Dataset
• Sedimen menjadi tuan rumah seng
• Laporan
• Sedimen menjadi tuan rumah tembaga
• Laporan
• Abstrak
• Unsur -unsur tanah jarang karbonatit
  • kertas

Limbah tambang

• Limbah tambang Australia

Judul asli

• Pengadilan Judul Asli Nasional

Penginderaan jauh

• Landsat Bare Earth - Bare Earth Median dari Landsat
• Citra Barest Earth Landsat Barest untuk Pemodelan Tanah dan Litologis: Dataset -> Detail Peningkatan
• Global Mining Footprint dipetakan dari citra satelit resolusi tinggi ** kertas
• Dem -> Australia 1 detik SRTM DEM dari berbagai varietas

Struktur

• Batas kerak utama Australia - edisi 2024

Kecepatan

• Au Tomo -> Model kecepatan generasi berikutnya dari kerak Australia dari pencitraan kebisingan ambien sinkron dan asinkron

Topografi

• Posisi topografi multiskala - RGB
• Informasi
• Indeks basah topografi - 1 dan 3 detik busur
• Informasi
• Indeks posisi topografi - 1 dan 3 detik busur
• Informasi
• Model intensitas pelapukan
  • kertas
• Informasi
• {Info] (https://researchdata.edu.au/weathering-intensity-model-australia/1361069)

Utara

• Ketebalan Tutup Tisa -> Titik Ketebalan Penutup untuk Tennant Creek Mt Isa dengan kisi -kisi interpolasi
• Pemetaan Konduktivitas Resolusi Tinggi Menggunakan Survei AEM Regional dan Pembelajaran Mesin -> Interpolasi Konduktivitas ML untuk Ausaem
• Abstrak diperpanjang
• Geologi Solid -> Geologi Solid Kraton Australia Utara
• Model inversi -> Model Gravitasi 3D Craton Australia Utara dan Model Inversi Magnetik
• Ni-Cu-PGE-> Potensi untuk deposit Ni-Cu-PGE sulfida yang diselenggarakan di Australia: analisis skala benua dari prospektivitas sistem mineral
• TISA IOCG -> Penilaian Potensi Mineral Besi Oksida Gold (IOCG) untuk Tennant Creek -Mt Isa Wilayah: Data Geospasial
• TISA Alteration -> memproduksi proxy alterasi magnetit dan hematit menggunakan gravitasi 3D dan inversi magnetik

Australia Selatan

Geologi

• Geologi Bedrock
• Crystalline Basement -> Crystalline Basements Potongan Lubang Bor
• Tambang dan deposit mineral
• Lubang bor mineral
• Geologi padat 3d
• 100K kesalahan
• Archaean
• Kesalahan archaean
• Mesoproterozoikum -> tengah
• Mesoproterozoikum -> kesalahan tengah
• Mesoproterozoikum -> terlambat
• Kesalahan Mesoproterozoik
• Neoproterozoikum
• Kesalahan neoproterozoikum
• Stuart Shelf Sedimentary Copper 3D Model
• Geologi Permukaan

Geofisika

• Auslamp 3D -> inversi magnetotelluric
• GCAS -> Gawler Craton Airborne Survey
• Gravity -> Gravity Grids
• Stasiun -> Stasiun Gravitasi
• Magnetika -> Magnetika
• Garis seismik -> garis seismik

Gawler

• Gawler MPP -> Proyek Promosi Mineral Gawler - Data

Queensland

• Ringkasan
• Deep Mining Queensland-> Deep Mining Queensland
• Deposit Atlas -> Atlas Deposit Provinsi Mineral Barat Laut
• GEOLOGI -> Tinjauan Seri Geologi
• Laporan Mineral dan Energi -> Laporan Provinsi Mineral dan Energi Queensland Barat Laut 2011 -NWQMEP
• Vektor -> vektor geokimia mineral
• Sumur minyak bumi
• Sumur gas jahitan batu bara
• Lubang bor

Cloncurry

• Toolkit -> Multielement Toolkit dan Laboratorium

Wilayah Utara

• Arunta IOCG-> Potensi Potensi Besi Oksida-Gold dari Wilayah Arunta Selatan
• Uranium Selatan -> Uranium Northern Northern Northern Uranium dan Paket Data Penilaian Sistem Energi Panas Bumi
• Tennant Creek -> model konduktivitas yang berasal dari data magnetotelluric di wilayah Tennant Timur, Wilayah Utara

New South Wales

Geologi

• GEOLOGI MELAMBIL -> Paket Data Geologi NSW yang mulus (versi lama juga di halaman ini)

Paket Data Potensi Mineral

• Curnamona
• Lachlan Timur
• Lachlan Tengah
• Southern New England

Australia Barat

Geokimia

• Mafic/ultramafic

Geologi

• Bedrock 100K
• Lembar peta 100K untuk permukaan yang harus Anda unduh secara individual dan gabungkan - mereka tidak konsisten
• 250K Mapsheet untuk permukaan yang harus Anda unduh secara individual dan gabungkan - mereka tidak konsisten
• 500k Bedrock
• Tambang yang ditinggalkan
• Kejadian mineral

Potensi mineral

• Nikel yang di-host-host
• Laporan

Prospektivitas

• Capricorn-> Analisis Prospektivitas Menggunakan Pendekatan Sistem Mineral - Proyek Studi Kasus Capricorn
• King Leopold -> Prospektif Mineral Rak Raja Leopold Orogen dan Lennard: Analisis Data Lapangan Potensial di Wilayah Kimberley Barat
• Emas yilgarn
• Yilgarn 2 -> Penemuan Mineral Prediktif di Yilgarn Craton Timur: Contoh penargetan skala distrik dari sistem mineral emas orogenik
• [Catatan Toko] -> WA memiliki beberapa paket prospektif yang tersedia untuk dibeli di USB Drive dengan harga jenis 50-60AU -lihat di bagian peta Geospaital

Tasmania

Geologi

• 250k
• 500k
• 25K
• Kejadian mineral
• Model 3D

Victoria

• Geologi yang mulus

Selandia Baru

• Paket Data Mineral -> Paket Data Eksplorasi Mineral

Amerika Utara

• Data dan kisi -kisi sumber daya geofisika, geologis, dan mineral nasional -> juga memiliki beberapa data Australia
• Sumur Air Tanah -> Database
• Orientasi tegangan horizontal maksimum dan besarnya stres stres (rezim patahan) di seluruh Amerika Utara

Kanada

Geologi

• Peta
• Geologi -> Peta Geologi Bedrock yang Diperbarui
• Geologi -> Kompilasi Geologi Bedrock dan Sintesis Regional Rae Selatan dan Bagian Domain Hearne, Provinsi Churchill, Wilayah Barat Laut, Saskatchewan, Nunavut, Manitoba dan Alberta
• Moho -> Basis Data Nasional Moho Estimasi Estimasi dari Refraksi Seismik dan Survei Teleseismik

Geofisika

• Pencarian DAP -> Pencarian Geoportal - Catatan dengan menjengkelkan ini ada di grid Geosoft - lihat Elsewere untuk kemungkinan konversi
• [Gravitasi, Magnetika, Radiometrik] -> Sebagian besar skala negara

Eropa

Finlandia

• FODD -> Deposit Mineral Fennoscandian

Irlandia

• MPM -> Proyek Pemetaan Potentinal Mineral

Makalah dengan kode

NLP

• https://www.sciencedirect.com/science/article/pii/S2590197422000064?via%3Dihub#bib20- -> Geoscience language models and their intrinsic evaluation -> NRCan code above [includes model]
• https://www.researchgate.net/publication/334507958_word_embeddings_for_application_in_geosciences_development_evaluation_and_examples_of_soil -related_concepts -> geovec [termasuk model]
• https://www.researchgate.net/publication/347902344_portuguese_word_embeddings_for_the_and_and_gas_industry_development_and_evaluasi -> Petrovec [termasuk model]
• Sumber Daya untuk Pencarian Otomatis dan Pengumpulan Dataset Geokimia dari Suplemen Jurnal

Geokimia

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

Geologi

• https://github.com/sydney-machine-learning/autoencoders_remotesensing-> stacked autoencoders untuk pemetaan litologis

Mineral

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

Makalah dengan fitur data

• Ini Anda dapat mereproduksi output secara geospasial dari data yang diberikan.

Prospektif Mineral

• https://www.sciencedirect.com/science/article/pii/s016913682100010x#s0135 -> pemodelan prospektivitas Ni magmatik Kanada (± Cu ± Co ± PGE) Sulphide Mineral Sistem [sangat layak dibaca]]
• https://www.sciencedirect.com/science/article/pii/s0169136821006612#b0510 -> pemodelan prospektif yang didorong oleh data -buatan Sedimen -Hosted Zn -PB dan bahan baku kritisnya [layak dibaca]]
• https://www.researchgate.net/publication/358956673_towards_a_duly_data-driven_prospectivity_mapping_methodology_a_case_study_of_the_southeastern_churchill_province_quebec_lad_labed_labed_labe_oreastern_churchill_province_leand_labec_labec_labe_labe_labe_labe_cuRure_churchill

Inggris

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

Geokimia

• https://www.researchgate.net/publication/361076789_automated_machine_learning_pipeline_for_geochemical_analysis

Geologi

• https://eprints.utas.edu.au/32368/ -> pemodelan litologi dan metasomatisme yang dibantu mesin

Geofisika

• https://github.com/tomasnaprstek/aeromagnetic_cnn - aeromagnetic cnn
• Kertas https://www.researchgate.net/publication/354772176_convolution_neural_networks_applied_to_the_interpretation_of_liniaMents_in_aeromagnetic_data
• PhD -> Metode baru untuk interpolasi dan interpretasi kelurusan dalam data aeromagnetik
• Kertas https://www.researchgate.net/publication/354772176_convolution_neural_networks_applied_to_the_interpection_of_liniaments_in_aeromagnetic_data -> jaringan saraf yang diterapkan pada interpretasi garis tirian di dalam linionom indata -> jaringan saraf yang diterapkan pada garis tata garis linia indata dalam linio incnetic incoulments di dalam lineacrom incerata incerata interomagnetic.

Output Geospasial - Tidak Ada Kode

• https://geoscience.data.qld.gov.au/report/cr113697 -> eksplorasi mineral dan pemetaan geologis yang didorong oleh data NWMP [CSIRO juga]

Jurnal

• https://www.sciencedirect.com/journal/artificial-intelligence-in-geosciences-> kecerdasan buatan dalam geosciences

Dokumen

• Umumnya bukan ML, atau tidak ada kode/data dan terkadang tidak ada ketersediaan sama sekali
• Akhirnya akan terpisah menjadi hal -hal yang memiliki paket data atau tidak seperti studi zona NSW.
• Namun, jika tertarik pada suatu area, Anda sering dapat membuat gambar jika tidak ada yang lain sebagai panduan kasar.
• Secara umum ini tidak dapat direproduksi - beberapa seperti studi zona prospektif NSW dan NWQMP dengan beberapa pekerjaan.
• Kertas sesekali di bagian ini dapat tercantum di atas

Baru untuk mengajukan

Umum

• 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-generasi baru algoritma kecerdasan buatan untuk pemetaan prospektivitas mineral
• https://www.researchgate.net/publication/235443297_addressing_challenges_with_exploration_datasets_to_generate_usable_mineral_potential_maps
• https://www.researchgate.net/publication/330077321_an_improved_data-driven_multiple_criteria_decision-making_procedure_for_spatial_modeling_of_mineral_prospectivity_adiaption_ofedictions
• Kecerdasan Buatan untuk Eksplorasi Mineral: Tinjauan dan Perspektif tentang Arah Masa Depan dari Ilmu Data -> https://www.sciencedirect.com/science/article/pii/s0012825224002691
• https://www.researchgate.net/project/bayesian-machine-learning-for-geological-modeling-and-geophysical-smegmentation
• https://www.researchgate.net/publication/229714681_classifiers_for_modeling_of_mineral_potential
• https://www.researchgate.net/publication/352251078_data_analysis_methods_for_prospectivity_modelling_as_applied_to_mineral_exploration_targeting_state-of-t-the-and_outlook
• https://www.researchgate.net/publication/267927728_data-driven_evidential_belief_modeling_of_mineral_potential_using_few_prospects_and_evidence_with_missing_value
• https://www.linkedin.com/pulse/deep-learning-meets-downward-continuation-caldera-analytics/?trackingid=ybkv3ukni7ygh3irchzdgw%3d%3d
• https://www.researchgate.net/publication/382560010_dinov2_rocks_geological_image_analysis_classification_segmentation_and_interpretability
• https://www.researchgate.net/publication/368489689_discrimination_of_pb-zn_deposit_types_using_sphalerite_goremistry_new_insights_from_machine_learning_algorithm
• https://link.springer.com/article/10.1007/s11430-024-1309-9-> model kecerdasan buatan yang dapat dijelaskan untuk pemetaan prospektivitas 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/33997675_ly_reversible_neural_networks_for_large-scale_surface_and_sub-surface_characterization_via_remote_sensing
  • arxiv
  • presentasi
  • konferensi
  • Juliacon
• https://www.researchgate.net/publication/220164488_geocomputation_of_mineral_exploration_targets
• https://www.researchgate.net/publication/272494576_geological_knowledge_discovery_and_minerals_targeting_from_regolith_using_a_machine_learning_approach
• https://www.researchgate.net/publication/280013864_geometric_average_of_spatial_evidence_data_layers_a_gis berbasis_multi- criteria_decision-making_approach_to_mineral_prospectivity_mapping
• https://www.researchgate.net/publication/355467413_harnessing_the_power_of_artificial_intelligence_and_machine_learning_in_mineral_exploration-opportunity_and_cautionary_notes
• https://www.researchgate.net/publication/335819474_importance_of_spatial_predictor_variable_selection_in_machine_learning_applications_-moving_from_data_reproduction_spatial_prediction
• https://www.researchgate.net/publication/337003268_improved_supervised_classification_of_bedrock_in_areas_of_transported_overburden_applying_domain_expertise_at_kerkasha_eritrea - gazley
• https://www.researchgate.net/publication/360660467_lithospheric_conductors_reveal_source_regions_of_convergent_margin_mineral_systems
• https://api.research-repository.uwa.edu.au/portalfiles/portal/5263287/Lysytsyn_Volodymyr_2015.pdf (PhD thesis) GIS-based epithermal copper prospectivity mapping of the Mt Isa Inlier, Australia: Implications for exploration targeting
• https://www.researchgate.net/publication/374972769_knowledge_and_technology_transfer_in_and_beyond_mineral_exploration -> Transfer Pengetahuan dan Teknologi Dalam dan di luar eksplorasi mineral dan mineral
• https://www.researchgate.net/publication/331946100_machine_learning_for_data-driven_discovery_in_solid_earth_geoscience
• https://theses.hal.science/tel-04107211/document-pendekatan pembelajaran mesin untuk sumber heterogen geologis sub-surface
• 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_evaluate_evidential_maps_for_mineral_prospectivity_modeling
• https://www.researchgate.net/publication/354925136_soil-sample_geochemistry_normaliced_by_class_membership_from_machine-learnnt_clusters_of_satellite_and_geophysics_data [gazley/hoodelite/howellite_and_geophysics.
• https://link.springer.com/article/10.1007/s12665-024-11870-1-> Kuantifikasi ketidakpastian peta geoscientif
• https://www.researchgate.net/publication/235443294_the_effect_of_map-scale_on_geological_complexity
• https://www.researchgate.net/publication/235443305_the_effect_of_map_scale_on_geological_complexity_for_computer-aided_exploration_targeting
• https://link.springer.com/article/10.1007/s11053-024-10322-8-> ketidakpastian yang diinduksi oleh alur kerja dalam pemetaan prospektivitas mineral yang didorong data-data

Prospektif Mineral

Australia

• https://www.mdpi.com/2072-4292/15/16/4074-> Pendekatan yang digerakkan oleh data spasial untuk pemetaan prospektivitas mineral
• https://www.researchgate.net/publication/353253570_a_truly_spatial_random_forests_algorithm_for_geoscience_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_unctintyty_on_prospectivity_predictions
• https://www.researchgate.net/publication/352310314_central_lachlan_mineral_potential_study
• https://meg.resourcesregulator.nsw.gov.au/sites/default/files/2024-05/eith%202024%20muller_exploration_in_the_house_keynote.pdf -> Mineral Kritis -Pemetaan Prospektivitas Menggunakan AI generatif.keynote.pdf -> Mineral Kritis -Pemetaan Prospektivitas Menggunakan AI Generatif.
• https://www.tandfonline.com/doi/pdf/10.1080/22020586.2019.12073159?needAccess=True -> Mengintegrasikan pendekatan sistem mineral dengan pembelajaran mesin: studi kasus 'eksplorasi mineral modern' di mt woods inlier - utara Gawler Craton, Australia Selatan
• https://www.researchgate.net/publication/365697240_mineral_potential_modelling_of_orogenic_gold_systems_in_the_granites-tanami_orogen_northern_territory_australia_a_multi-tanami_orogen_northern
• https://publications.csiro.au/publications/publication/picsiro:ep2022-0483 -> tanda tangan sistem mineral utama di provinsi ISA Gunung Timur, Queensland: Perspektif Baru dari Analisis Data dari Analisis Data
• https://link.springer.com/article/10.1007/s11004-021-09989-z -> Stochastic Modelling of Mineral Exploration Targets
• https://www.researchgate.net/publication/276171631_Supervised_Neural_Network_Targeting_and_Classification_Analysis_of_Airborne_EM_Magnetic_and_Gamma-ray_Spectrometry_Data_for_Mineral_Exploration
• 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

Brazil

• 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

Kusut

• 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

Kanada

• 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

Afrika Tengah

• 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

Chili

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

Cina

• 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
• https://www.researchgate.net/publication/347079505_Convolutional_neural_network_and_transfer_learning_based_mineral_prospectivity_modeling_for_geochemical_exploration_of_Au_mineralization_within_the_Guandian-Zhangbaling_area_Anhui_Province_China
• 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
• https://www.researchgate.net/publication/352893038_Detection_of_geochemical_anomalies_related_to_mineralization_using_the_GANomaly_network
• https://www.researchgate.net/publication/357685352_Determination_of_Predictive_Variables_in_Mineral_Prospectivity_Mapping_Using_Supervised_and_Unsupervised_Methods
• https://www.sciencedirect.com/science/article/abs/pii/S0375674221001370 -> Distinguishing IOCG and IOA deposits via random forest algorithm based on magnetite composition
• https://www.researchgate.net/publication/340401748_Effects_of_Random_Negative_Training_Samples_on_Mineral_Prospectivity_Mapping
• 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
• https://www.researchgate.net/publication/379852209_Fractal-Based_Multi-Criteria_Feature_Selection_to_Enhance_Predictive_Capability_of_AI-Driven_Mineral_Prospectivity_Mapping
• https://www.researchgate.net/publication/338789096_From_2D_to_3D_Modeling_of_Mineral_Prospectivity_Using_Multi-source_Geoscience_Datasets_Wulong_Gold_District_China
• 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, Cina
• https://www.researchgate.net/publication/361194407_Visual_Interpretable_Deep_Learning_Algorithm_for_Geochemical_Anomaly_Recognition

Mesir

• 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

Inggris

• 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

Eritrea

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

Finlandia

• 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

Finlandia

• 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

Ghana

• 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

Tanah penggembalaan

• 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

India

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

Indonesia

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

Iran

• 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]

Irlandia

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

India

• 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

Korea

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

Norwegia

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

Korea Selatan

• 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

Filipina

• 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

Rusia

• 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

Afrika Selatan

• 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

Spanyol

• 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

Sudan

• 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]

Swedia

• 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

Tanzania

• 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

Inggris Raya

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

Amerika Serikat

• 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

Zambia

• 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

Zimbabwe

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

GENERAL PAPERS

Overviews

• 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

Deposito

• 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 Lithium

Geochemistry

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

Apatite

• 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

Geologi

Perubahan

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

Kedalaman

• 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

Umum

• 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

Mineralogi

• 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

Stratigraphy

• 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

Struktur

• 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

Geofisika

Dasar

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

Umum

• 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

Gaya berat

• 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]

Kemaknitan

• 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

Seismik

• 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

Ketakpastian

• 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

Peta

• 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

NLP

• 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

Petrography

• 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

Ringkasan

• 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

Geochemistry

• 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

Kusut

• 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

Ketakpastian

• 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

Australia

• 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

tidak

• 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

Brazil

• 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

Australia

• 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

tidak

• 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

negara bagian baru

• 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

Brazil

• 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

Australia

• 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

New South Wales

• 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

Victoria

• 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.

Jaringan Syaraf

• 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

Umum

• 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

Afrika

• 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

Brazil

• 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

Cina

• 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

Tanah penggembalaan

• 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

India

• 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

Iran

• 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

Spanyol

• 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

Lainnya

• 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

NLP

• 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

Pembelajaran Mendalam

• 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