mineral exploration machine learning

Mineralexploration-maschinelles Lernen

Auf dieser Seite werden Ressourcen für die Mineralexploration und maschinelles Lernen aufgeführt, im Allgemeinen mit nützlichem Code und Beispielen. ML und Data Science sind ein riesiges Feld. Dies sind Ressourcen, die ich in der Praxis als nützlich und/oder interessant empfunden habe. Derzeitige Links zu einem Zweig eines Repositorys sind darauf zurückzuführen, dass ich etwas zur Verwendung geändert und als Referenz in eine Liste aufgenommen habe. Es werden auch Ressourcen für die Datenanalyse, -transformation und -visualisierung bereitgestellt, da dies den größten Teil der Arbeit ausmacht.

Vorschläge willkommen: Eröffnen Sie eine Diskussion, ein Problem oder eine Pull-Anfrage.

Inhaltsverzeichnis

• Perspektive
• Geologie
• Verarbeitung natürlicher Sprache
• Fernerkundung
• Datenqualität
• Gemeinschaft
• Cloud-Anbieter
• Domänen
• Überblick
• Webdienste
• Datenportale
• Werkzeuge
• Ontologien
• Bücher
• Datensätze
• Papiere
• Andere
• Allgemeines Interesse

Karte

• Karte von Github

Rahmenwerke

• UNCOVER-ML-Framework
  • Geo-Wavelets
  • ML-Vorverarbeitung
  • GIS ML-Workflow
• EIS Toolkit -> Python-Bibliothek für die Kartierung der Mineralvorkommen aus dem EIS Horizon EU-Projekt
• PySpatialML -> Bibliothek, die die Vorhersage und Handhabung für maschinelles Rasterlernen erleichtert, automatisch zum Geotiff usw.
• scikit-map
• TorchGeo -> Pytorch-Bibliothek für Modelle im Fernerkundungsstil
• terratorch -> Flexibles Feinabstimmungs-Framework für Geospatial Foundation Models
• TorchSpatial
  • Papier
• geodl
• Geo Deep Learning -> Einfaches Deep-Learning-Framework basierend auf RGB
• AIDE: Künstliche Intelligenz zur Entwirrung von Extremen
• ExPloRA -> ExPLoRA: Parametereffizientes erweitertes Vortraining zur Anpassung von Vision Transformern unter Domänenverschiebungen
  • Webseite
  • Papier
• (https://www.researchgate.net/profile/Miguel-Angel-Fernandez-Torres/publication/381917888_The_AIDE_Toolbox_Artificial_intelligence_for_d isentangling_extreme_events/links/66846648714e0b03153f38ae/The-AIDE-Toolbox-Artificial-intelligence-for-disentangling-extreme-events.pdf)

R

• CAST -> Caret-Anwendungen für räumlich-zeitliche Modelle
• geodl -> semantische Segmentierung von Geodaten mithilfe von Deep Learning auf Basis eines Faltungs-Neuronalen Netzwerks
  • Papier

Pipelines

• Geotargts -> Erweiterung der Ziele auf Terra und Sterne

Perspektive

Australien

• Potenzialkarten für Eisenoxid-Kupfer-Gold-Minerale
• Maschinelles Lernen für die geologische Kartierung: Algorithmen und Anwendungen -> Doktorarbeit mit Code und Daten
• Prospektive Kartierung von Ni-Co-Lateriten
• Transform 2022 Tutorial -> Beispiel für eine zufällige Gesamtstruktur
  • Video
• Zinn-Wolfram
  • Zusammenarbeit
• Raumzeitliche Erkundung von Porphyr-Kupfer
  • Papier
• minpot-toolkit -> Beispiel einer Lab-Grenzanalyse von Hoggard et al. mit sedimentärem Kupfer
• MPM-WofE -> Kartierung des Mineralpotenzials – Beweiskraft

Explorer-Challenge

• Explorer Challenge -> OZ Minerals veranstaltet Wettbewerb mit Einführung in Data Science

Südaustralien

• Gawler_MPM -> Kobalt, Chrom, Nickel
  • Papier
• Geophysikalische Datenclusterung im Gawler-Kraton
  • [Zenodo Data] (Automatisierte Erkennung mineralisierungsbedingter Kratonstrukturen mithilfe geophysikalischer Daten und unbeaufsichtigtem maschinellem Lernen)

Entdecken Sie SA – South Australian Department of Energy and Mining Competition

• Gewinner -> SARIG-Dateninformationen
• Caldera -> Caldera Analytics-Analyse
• IncertoData
• Butterworth und Barnett -> Butterworth und Barnett-Eintrag
• Datengesteuerte Mineralisierungskartierung

Nordamerika

Kanada

• Transferperspektive lernen
  • Papier -> Porphyr-Typ-Mineralprospektionskartierung mit unausgeglichenen Daten durch vorheriges geologisches Transferlernen

Südamerika

• Maschinelles Lernen zur Klassifizierung von Erzlagerstätten anhand tektonomagmatischer Eigenschaften

Brasilien

• Mapa Preditivo -> Brasilien-Studentenprojekt
• Course_Predictive_Mapping_USP -> Kursprojekt
• Kartierung der Mineralvorkommen
• 3D-Beweisgewichte
• Geologische Komplexität SMOTE -> beinhaltet fraktale Analyse
  • Papier
• MPM Jurena -> Mineralprovinz Jurena

China

• MPM durch Ensemble-Lernen -> Qingchengzi Pb-Zn-Ag-Au-Polymetallbezirk China
• Mineral Prospectivity Prediction Convolutional Neural Networks -> CNN-Beispiel mit einigen Architekturen [ein Artikel dieses Autors verwendet GoogleNet]
• Vorhersage der Mineralaussichten durch CSAE
• Vorhersage der Mineralaussichten durch CAE
  • Papier

Sudan

• Mineralprospektivitätskartierung ML

Norwegen

• Ein auf maschinellem Lernen basierender Ansatz zur regionalen Kartierung empfindlicher glaziomariner Tone, der luftgestützte Elektromagnetik und geotechnische Daten kombiniert
  • Papier

Geologie

• Prädiktive geologische Karten Brasiliens -> Arbeit des brasilianischen geologischen Dienstes
• Tiefe zum Grundgestein (Bewertung räumlich aktivierter maschineller Lernansätze für die Kartierung der Tiefe zum Grundgestein)
  • Papier
• DL-RMD -> Eine geophysikalisch eingeschränkte Datenbank für elektromagnetische Widerstandsmodelle für Deep-Learning-Anwendungen
• Geologischer Bildklassifikator
• Geologische Kartierung im Zeitalter der künstlichen Intelligenz -> Geologische Kartierung im Zeitalter der künstlichen Intelligenz
• GeolNR -> Geologische implizite neuronale Darstellung für dreidimensionale strukturelle geologische Modellierungsanwendungen
  • Papier
• mapeamento_litologico_preditivo
• Kartierung globaler Druck-Temperatur-Bedingungen im lithosphärischen Mantel durch maschinell lernende Thermobarometrie
  • Papier
• Neural Rock Typing
• West Musgraves Geology Uncertainty -> Unsicherheitskartenvorhersage mit Entropieanalyse: sehr nützlich
• Transformator zur Nichtstationaritätsminderung
  • Zusammenarbeit -> Notizbuch
    • Papier
• Grundgestein vs. Sediment
  • Papier
• autoencoders_remotesensing
  • Papier -> Fernerkundungsrahmen für geologische Kartierung über gestapelte Autoencoder und Clustering

Trainingsdaten

• Into the Noddyverse -> ein riesiger Datenspeicher mit geologischen 3D-Modellen für maschinelles Lernen und Inversionsanwendungen
  • Zenondo-Repository
  • Webseite

Lithologie

• Deep-Learning-Lithologie
• Gesteinsprotolith-Prädiktor
• SA Geology Lithology Vorhersagen
• Automatisierte Bohrlochprotokollkorrelation
• dawson-facies-2022 -> Transferlernen für geologische Bilder
  • Papier -> Einfluss der Datensatzgröße und der Architektur eines Faltungs-Neuronalen Netzwerks auf das Transferlernen für die Klassifizierung von Karbonatgesteinen
• Litho-Klassifizierung -> Klassifizierung vulkanischer Fazies mithilfe von Random Forest
• Ansatz für maschinelles Lernen mit mehreren Ansichten von Ensembles für die 3D-Modellierung unter Verwendung geologischer und geophysikalischer Daten
  • Papier
• SedNet
  • Papier

Bohren

• Heterogenes Bohren – Nicta/Data61-Projektbericht zur Betrachtung der Modellierung unter Verwendung von Bohrlöchern, die nicht weit genug gehen
• corel -> Intelligentes Computer-Vision-Modell, das Fazies identifiziert und Gesteinstypen auf Kernbildern durchführt

Paläotäler

• Sub3DNet1.0: ein Deep-Learning-Modell für die regionale 3D-Untergrundstrukturkartierung

Stratigraphie

• Predicatops -> Stratigraphische Vorhersage für Kohlenwasserstoffe
• stratal-geometries -> Vorhersage stratigraphischer Geometrien aus unterirdischen Bohrlochprotokollen

Strukturell

• APGS -> Strukturgeologiepaket
• Bewertung von Plattenrekonstruktionsmodellen mithilfe von Plattenantriebskraft-Konsistenztests -> Jupyter-Notizbuch und Daten
  • Papier
• gplately
• [Strukturgeologie-Kochbuch](https://github.com/gcmatos/structural-geology-cookbook]
• GEOMAPLEARN 1.0 -> Erkennung geologischer Strukturen aus geologischen Karten mit maschinellem Lernen
• Lineament-Lernen -> Fehlervorhersage und -kartierung durch potenzielles Feld-Deep-Learning und Clustering
• LitMod3D -> 3D integrierte geophysikalisch-petrologische interaktive Modellierung der Lithosphäre und des darunter liegenden oberen Mantels
• andere

Simulation

• GebPy -> Generierung geologischer Daten für Gesteine ​​und Mineralien
• OpenGeoSys -> Entwicklung numerischer Methoden zur Simulation thermo-hydro-mechanisch-chemischer (THMC) Prozesse in porösen und gebrochenen Medien
• Stratigraphics.jl -> Erstellen einer 3D-Stratigraphie aus 2D-geostatistischen Prozessen

Geodynamik

• Badlands -> Becken- und Landschaftsdynamik
• CitcomS -> Finite-Elemente-Code zur Lösung komprimierbarer thermochemischer Konvektionsprobleme im Zusammenhang mit dem Erdmantel.
• LaMEM -> Simulation verschiedener thermomechanischer geodynamischer Prozesse wie der Wechselwirkung zwischen Mantel und Lithosphäre
• PTatin3D -> Untersuchung langfristiger Prozesse, die für die Geodynamik relevant sind [ursprüngliche Motivation: Toolkit zur Untersuchung hochauflösender, dreidimensionaler Modelle der lithosphärischen Verformung]
• Unterwelt -> Finite-Elemente-Modellierung der Geodynamik

Geophysik

Foundation-Modelle

• Cross-Domain Foundation Model Adaption: Bahnbrechende Computer-Vision-Modelle für die geophysikalische Datenanalyse -> einige Codes folgen noch
  • Papier
• Seismisches Fundamentmodell -> „ein Deep-Learning-Modell der neuen Generation in der Geophysik“

Australien

Regolithtiefe

• Regolithtiefe -> Modell
• Vollständiges radiometrisches Gitter Australiens mit modellierter Füllung

AEM-Interpolation

• Hochauflösende Leitfähigkeitskartierung mittels regionaler AEM-Untersuchung
  • Abstrakt

Elektromagnetik

• TEM-NLnet: Ein Deep Denoising-Netzwerk für transiente elektromagnetische Signale mit Noise Learning

Umkehrung

• Maschinelles Lernen und geophysikalische Inversion -> rekonstruieren Sie den Artikel von Y. Kim und N. Nakata (The Leading Edge, Band 37, Ausgabe 12, Dezember 2018)

Euler-Entfaltung

• https://legacy.fatiando.org/gallery/gravmag/euler_moving_window.html
  • Endlich eine Mundharmonika-Version? 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

Schwerkraft

• [Wiederherstellung des 3D-Kellerreliefs mithilfe von Schwerkraftdaten durch Faltungs-Neuronale Netze]
  • Daten
  • Papier
• Stabile Fortsetzung des Gravitationspotentialfeldes nach unten, implementiert durch Deep Learning
  • Papier
• Schnelle Bildgebung für 3D-Dichtestrukturen durch maschinellen Lernansatz
  • Papier

Magnetik

• Hochauflösende aeromagnetische Karte durch Adapted-SRGAN
  • Papier
• MagImage2Geo3D
  • Papier

Seismisch

• StorSeismic -> Ein Ansatz, um ein neuronales Netzwerk vorab zu trainieren, um seismische Datenmerkmale zu speichern
• PINNtomo -> Seismische Tomographie unter Verwendung physikinformierter neuronaler Netze
  • Papier

Seismologie

• obspy -> Framework für die seismologische Verarbeitung

Petrophysik

• ML4Rocks -> Einige Intro-Arbeiten

Tektonik

• Erkennen Sie die Ablösung der subduzierenden Platte in einer alten Subduktionszone mithilfe von maschinellem Lernen -> Notizbuch
  • Feigenaktie
• Colab-Notizbuch -> Google Colab-Eingabedatei für Benchmark-Ergebnisse der ML-SEISMIC-Veröffentlichung
  • Papier
• Die Kraft des maschinellen Lernens in der Geodynamik freisetzen
  • Diplomarbeit im Zusammenhang mit Auszeichnung
• Physikalisch-informierte neuronale Netze zur Fehlerschlupfsimulation mit Geschwindigkeits- und Zustandsreibungsgesetz
  • Simulation und Reibungsparameterschätzung bei langsamen Schlupfereignissen
  • Papier -> Physikinformiertes Deep Learning zur Schätzung der räumlichen Verteilung von Reibungsparametern in Regionen mit langsamem Gleiten

Geochemie

• CODAinPractice -> Kompositionsdatenanalyse in der Praxis
• GeoCoDa
• DAN-GRF -> Tiefes Autoencoder-Netzwerk, verbunden mit einem geografischen Zufallswald zur räumlich bewussten Erkennung geochemischer Anomalien
  • Papier
• Dash Geochemical Prospection -> Web-App zur Klassifizierung von Bachsedimenten mit K-Mitteln
• Verbesserung der maschinellen Lernthermobarometrie für Clinopyroxen-haltige Magmen
  • Papier -> Enhancing-ML-Thermobarometry-for-Clinopyroxene-Lager-Magmen
• Zirkonfruchtbarkeitsmodelle -> Entscheidungsbäume zur Vorhersage des fruchtbaren Zirkons aus Porphyr-Kupfer-Lagerstätten
  • Papier
• Werkzeug für maschinelles Lernen von Zirkonspurenelementen zur Vorhersage der Art und Größe der Porphyrlagerstätte
  • Zenodo-Archiv
  • Papier
• geology_class0 -> Ein maschineller Lernansatz zur Unterscheidung magmatischer Gesteine ​​und Erzvorkommen anhand von Zirkon-Spurenelementen
  • Papier
  • Demo-Anwendung
  • https://colab.research.google.com/drive/1-bOZgG6Nxt2Rp1ueO1SYmzIqCRiyyYcT
• GeochemPrint
  • Papier
• Globale Geochemie
• ICBMS Jacobina -> Analyse der Pyritchemie einer Goldlagerstätte
• Interpretation der Spurenelementchemie von Zirkonen aus Bor und Cukaru Peki: konventioneller Ansatz und zufällige Waldklassifizierung
• Indicator_minerals -> Kann PCA die Geschichte des Ursprungs von Turmalin erzählen?
• Journal of Geochemical Exploration – Manifold
• LewisML -> Analyse der Lewis-Formation
• Glimmer -> Chemische Zusammensetzung, in glänzend
• Multivariate statistische Analyse und maßgeschneiderte Abweichungsnetzwerkmodellierung zur geochemischen Anomalieerkennung von Seltenerdelementen
  • Papier
• Prospektivitätskartierung von Seltenerdelementen durch geochemische Datenanalyse -> Prospektivitätskartierung von Seltenerdelementen durch geochemische Datenanalyse
• QMineral Modeller -> Virtueller Assistent für Mineralchemie des brasilianischen geologischen Dienstes
• Weltliche Veränderungen im Auftreten der Subduktion während des Archäikums -> Zenodo-Codearchiv
  • [Papier] https://www.researchgate.net/publication/380289934_Secular_Changes_in_the_Occurrence_of_Subduction_During_the_ArcheanEin maschineller Lernansatz zur Unterscheidung von magmatischen Gesteinen und Erzvorkommen durch Zirkonspurenelemente

Kriging

• DKNN: Deep-Kriging-Neuronales Netzwerk für interpretierbare Geodateninterpolation
  • Papier

Verarbeitung natürlicher Sprache

• Textextraktion -> Textextraktion aus Dokumenten: kostenpflichtiges ML als Service, funktioniert aber sehr gut, kann Tabellen effizient extrahieren
  • Großformat -> Großformatversion
• NASA-Konzept-Tagging -> Keyword-Vorhersage
  • API -> API-Webdienst
  • Präsentation
• Datenextraktor für Petrographieberichte
• SA-Explorationsthemenmodellierung -> Themenmodellierung aus Explorationsberichten
• Stratigraph
• Geokorpus
• Portugiesisches BERT
• BERT CWS
  • Papier
• Automatisierte Extraktion der Bohrlochergebnisse eines Bergbauunternehmens
  • Konferenzpapier

Worteinbettungen

• Geowissenschaftliche Sprachmodelle -> Verarbeitung von Code-Pipeline und Modellen [Glove, BERT], umgeschult auf geowissenschaftlichen Dokumenten aus Kanada
  • Datensätze -> Daten zur Unterstützung von Modellen
  • Papier -> Geowissenschaftliche Sprachmodelle und ihre intrinsische Bewertung
  • Papier -> Anwendungen der Verarbeitung natürlicher Sprache auf geowissenschaftliche Textdaten und Prospektivitätsmodellierung
• GeoVec -> Worteinbettungsmodell, trainiert anhand von 300.000 geowissenschaftlichen Arbeiten
  • GeoVec-Modell -> OSF-Speicher für GeoVec-Modell
  • Papier
  • GeoVecto Litho -> Interpolation von 3D-Modellen aus Worteinbettungen
  • GeoVEC Playground -> Arbeiten mit dem Padarian GeoVec-Handschuhworteinbettungsmodell
• GloVe -> Standford-Bibliothek zur Erstellung von Worteinbettungen
  • gloVE Python Glove, Glove-Python unter Windows höchst problematisch: Hier wird die Binärversion für Windows installiert:
• Fäustlinge -> Im Speicher vektorisierte Handschuhimplementierung
• PetroVec -> Portugiesische Worteinbettungen für die Öl- und Gasindustrie: Entwicklung und Bewertung
• WordembeddingsOG -> Portugiesische Worteinbettungen für Öl und Gas
• Portugiesische Worteinbettungen
  • Portugiesische Modelle
• Spanische Worteinbettungen
• Mehrsprachige Ausrichtung
  • Überblick über Ansätze

Anerkennung benannter Entitäten

• Geo-NER-Modell -> Erkennung benannter Entitäten
• GeoBERT – Hugging Face Repo für Model in
  • [Papier]https://www.researchgate.net/publication/359186219_Few-shot_learning_for_name_entity_recognition_in_geological_text_based_on_GeoBERT
• INDUS -> Auf die Wissenschaft zugeschnittene LLM-Suite der NASA
  • Papier
• So finden Sie mit Amazon Comprehend wichtige geowissenschaftliche Begriffe im Text, ohne NLP zu beherrschen
• OzRock – OzRock: Ein gekennzeichneter Datensatz zur Entitätserkennung im geologischen Bereich (Mineralienexploration).

Ontologie

• GAKG -> Ein multimodaler Geowissenschafts-Wissensgraph (Chinesisch)
• GeoERE-Net -> Verstehen geologischer Berichte basierend auf Wissensgraphen mithilfe eines Deep-Learning-Ansatzes
  • Papier
• GeoFault-Ontologie
• geosim -> Semantisch ausgelöste qualitative Simulation eines geologischen Prozesses
  • [https://www.duo.uio.no/handle/10852/111467](Knowledge Modeling for Digital Geology) -> Doktorarbeit mit zwei Arbeiten
  • SIRIUS GeoAnnotator -> Website-Beispiel von oben
• Ontologie CWS
• Stratigraphischer Wissensgraph (StraKG)
  • Papier

Große Sprachmodelle

• Großes Sprachmodell für Geowissenschaften
  • Aufsatz „Learning Foundation Language Models for Geoscience Knowledge Understanding and Utilization“.
• GeoGalactica -> Ein größeres grundlegendes Sprachmodell in den Geowissenschaften
  • Technischer Bericht
• GeoChat -> fundiertes Large Vision Language Model für die Fernerkundung
• LAGDAL -> LLM Zuordnung geologischer Karteninformationen zu Standortexperimenten

Chatbots

• GeoGPT -> Projekt der Deep Time Digital Earth Research Group aus China

Fernerkundung

• CNN Sentinel -> Übersicht über die Landnutzungsklassifizierung aus Satellitendaten mit CNNs basierend auf einem offenen Datensatz
• DEA-Notizbücher -> Beispiel für skalierbares maschinelles Lernen, aber viele nützliche Dinge hier
• EASI-Kochbuch-Notizbücher -> Einführungen in die CSIRO Earth Analytics-Plattform für ODC-Analysen
• DS_UNet -> Unet vereint Sentinel-1 Synthetic Aperture Radar (SAR) und Sentinel-2 Multispectral Imager
• Multi-Pretext-maskierter Autoencoder (MP-MAE)
• Daten
• segment-geospatial -> Alles für Geodatenzwecke segmentieren
• SamGIS -> Alles segmentieren, was auf GIS angewendet wird
• SatMAE++ -> Rethinking Transformers Vorschulung für multispektrale Satellitenbilder
• grid-mae -> Untersuchen Sie die Verwendung von Multiskalengittern in einem Vision Transformer Masked Autoencoder
• ScaleMae
  • Papier
• CIMAE -> CIMAE – Kanalunabhängiger maskierter Autoencoder
• fork -> um ihm den Namen als Referenz zu geben
• [Selbstüberwachtes Repräsentationslernen für die Fernerkundung] -> Die Masterarbeit umfasst das oben Genannte und Vergleiche mehrerer Modelle
• U Scheune
  • Papier
• Erdnetze
• GeoTorchAI -> GeoTorchAI: Ein raumzeitliches Deep-Learning-Framework
• [pytorcheo](https://github.com/earthpulse/pytorchEO -> Deep Learning für Erdbeobachtungsanwendungen und -forschung
• AiTLAS -> eine Open-Source-Benchmark-Suite zur Bewertung modernster Deep-Learning-Ansätze für die Bildklassifizierung in der Erdbeobachtung
• Segmentation Gym -> Gym ist als „One-Stop-Shop“ für die Bildsegmentierung auf „ND“ konzipiert – einer beliebigen Anzahl zusammenfallender Bänder in einem multispektralen Bild
• deep_learning_alteration_zones
• Fantastische Sammlung von Bandverhältnissen im Bergbau -> Sammlung einfacher Bandverhältnisanwendungen zur Hervorhebung verschiedener Mineralien
• Fantastische Fernerkundungsfundamentmodelle
• Clay -> Ein Open-Source-KI-Modell und eine Schnittstelle für die Erde
• IBM-NASA-GEOSPATIAL Prithvi
• Bildsegmentierung durch Feinabstimmung des Grundmodells -> Für Prithvi
• AM-RADIO: Agglomerative Vision Foundation-Modell
  • Papier -> - Reduzieren Sie alle Domänen auf eine
• RemoteCLIP -> Ein Vision Language Foundation-Modell für die Fernerkundung
• SpectralGPT
• zenodo) -> Fernerkundungsgrundlagenmodell, angepasst an Spektraldaten
  • Papier -> Unsichtbar

Verarbeitung

• ASTER-Konvertierung -> Konvertierung von ASTER hd5 in Geotiff NASA Github
• HLS-Datenressourcen -> Harmonisiertes Landsat-Sentinel-Wrangling
• Sarsen -> Xarray-basierte SAR-Bildverarbeitung und -korrektur
• openEO -> openEO entwickelt eine offene API, um R, Python, JavaScript und andere Clients mit EO-Cloud-Backends zu verbinden

Spektrale Entmischung

• Konventionelle-zu-Transformator-für-Hyperspectral-Image-Classification-Survey-2024
  • Papier
• Überprüfung des hyperspektralen Deep Learning
• Hyperspektrale Autoencoder
• Deeplearn HSI
• 3DCAE-Hyperspektral-Klassifizierung
• DeHIC
• Rev-Net
  • Papier -> Ein reversibles generatives Netzwerk für hyperspektrale Entmischung mit spektraler Variabilität
• Pysptools -> verfügt auch über nützliche heuristische Algorithmen
• Spektralpython
• Spektraldatensatz RockSL -> Spektraldatensatz öffnen
• Entmischung

Hyperspektral

• CasFormer: Kaskadierte Transformatoren für fusionsfähige rechnergestützte hyperspektrale Bildgebung
• Spektrale Normalisierung für Keras
  • Papier
• S^2HM^2 -> S2HM2: Ein spektralräumliches hierarchisches maskiertes Modellierungsframework für selbstüberwachtes Merkmalslernen und Klassifizierung großräumiger hyperspektraler Bilder
  • Papier

Visualisierung

• Tiefgreifende Farbkartenextraktion aus Visualisierungen
  • Papier
• Semantische Segmentierung zur Extraktion historischer Störungen im Tagebau aus topografischen Karten -> Beispiel für Kohlebergwerke
  • Papier
• Internationale chronostratigraphische Farbcodes -> RGB-Codes und andere in Tabellenkalkulations- und anderen Formaten
• LithClass -> USGS-Version der Lithologie-Farbcodes
• Farbversion
• SeisWiz -> Leichter Python-SEG-Y-Viewer

Textur

• Klassifizierung der Mineraltextur mithilfe tiefer Faltungs-Neuronalnetze: Eine Anwendung auf Zirkone aus Porphyr-Kupfer-Lagerstätten
  • Papier

Simulation

• Intelligent Prospector -> Sequentielle Datenerfassungsplanung
• Zenodo
  • Papier

Geometrie

• Deep Angle -> Schnelle Berechnung von Kontaktwinkeln in Tomographiebildern mittels Deep Learning

Andere

• Netzwerkanalyse mineralogischer Systeme
  • Daten -> Daten aus Papier hier
• Geoanalytik und maschinelles Lernen
• Maschinelles Lernen unter der Oberfläche
• ML Geowissenschaften
• Seien Sie ein geowissenschaftlicher Detektiv
• Earth ML -> Einige grundlegende Tutorials zu PyData-Ansätzen
• GeoMLA -> Algorithmen für maschinelles Lernen für räumliche und raumzeitliche Daten

Plattformen

Führer

• Geospatial CLI – Liste der Geospatial-Befehlszeilentools
• Satellitenbild-Deep-Learning
• Erdbeobachtung
• Künstliche Intelligenz der Erde
• Open Source GIS -> Umfassender Überblick über das Ökosystem

Datenqualität

• Geowissenschaftliche Datenqualität für maschinelles Lernen -> Geowissenschaftliche Datenqualität für maschinelles Lernen
• Australische Schwerkraftdaten -> Übersicht und Analyse der Schwerkraftstationsdaten
• Geodiff -> Vergleich von Vektordaten
• Redflag -> Analyse der Daten und Übersicht zur Erkennung von Problemen

Maschinelles Lernen

• Dask-ml -> Verteilte Versionen einiger gängiger ML-Algorithmen
  • Papier
• geospatial-rf -> Funktionen und Wrapper zur Unterstützung bei zufälligen Gesamtstrukturanwendungen in einem räumlichen Kontext
• Geospatial-ml -> Mehrere gängige Pakete gleichzeitig installieren

Latentraum

• Verschachtelte Fusion
  • Papier -> Nested Fusion: Dimensionsreduktion und latente Strukturanalyse mehrskaliger verschachtelter Daten für M2020 PIXL RGBU- und XRF-Daten

Metriken

• Ergebnisse -> Verifizierung und Bewertung von Modellen und Vorhersagen mit xarray

Wahrscheinlichkeit

• NG Boost -> probabilistische Regression
• Probabilistisches ML
• Absacken von PU mit BO -> Positives unbeschriftetes Absacken mit Bayes'scher Optimierung

Clustering

Selbstorganisierende Karten

• GisSOM -> Geodatenzentrierte selbstorganisierende Karten vom finnischen geologischen Dienst
• SimpSOM -> Selbstorganisierende Karten

Andere

• hdbscan
• kmedoide
  • kmedoide
• Picasso

Bayesianisch

• Bayseg -> Räumliche Segmentierung

Erklärbarkeit

• InterpretML -> Modelle tabellarischer Daten interpretieren
• InterpretML -> Community-Ergänzung

Tiefes Lernen

• Deep Colormap Extraction -> Versuch, eine Datenskala aus Bildern zu extrahieren
• Extrahieren und klassifizieren Sie Bilder aus geowissenschaftlichen Dokumenten

Daten

• Xbatcher -> Xarray-basiertes Datenlesen für Deep Learning
  • Cloud-native Datenlader für maschinelles Lernen mit Zarr und Xarray
• zen3geo -> Datenwissenschaft im Xbatcher-Stil mit Pytorch

Erklärbarkeit

• Werte gestalten
• Weight Watcher -> Analysieren Sie, wie gut Netzwerke trainiert sind
• Weightwatcher.ai
• Weightwatcher-ai.com -> Professionelle Webversion

Selbstüberwachtes Lernen

• Selbstüberwacht -> Pytorch-Lightning-Implementierungen mehrerer Algorithmen
• Simclr
• Tolles selbstüberwachtes Lernen -> Kuratierte Liste

Hyperparameter

• Hyperopt
• TPOT Automatisiertes ML

Codierungsumgebungen

• DEA-Sandbox
• Würfel in einer Box

Gemeinschaft

• Software Underground – Gemeinschaft von Menschen, die daran interessiert sind, die Schnittstelle zwischen Untergrund und Code zu erforschen
  • Chat-Anmeldung – SWUNG-Community-Chat-Anmeldung
  • Mattermost – Community-Chat-Dienst
    • Alter Slack-Kanal (veraltet, siehe Mattermost oben)
  • Geowissenschaftliche Open-Source-Anbindung
  • Videos
    • Transformation 2022
  • Fantastische offene Geowissenschaften [Anmerkung: Öl- und Gasvoreingenommenheit]
  • Transform 2021 Hacking-Beispiele
  • Segysak 2021 Tutorial
  • T21 Seismisches Notizbuch
  • Praktische Seismik mit Python
  • Transform 2021 Simpeg
• Pangeo
  • Forum
  • COG Best Practices
• Digital Earth Australien
  • Slack-Kanal
• Open Source Geospatial Foundation
• OSGeoLive -> Bootfähige DVD/USB mit vielen Open-Source-Geodatensoftware
• ASEG -> Videos von Geowissenschaftlern der Australia Society of Exploration
• KI für geologische Modellierung und Kartierung -> Videos vom Konferenztag
• Konferenz

Cloud-Anbieter

AWS

• ec2 Spot Labs -> Vereinfacht automatisch arbeitende Sith-Spot-Instanzen
• Sagemaker Geospatial ML
• Sagemaker -> ML Managed Service
  • SDK
  • Entrypoint-Dienstprogramme
  • Werkstatt 101
  • Schulungs-Toolkit

Charge

• Shepard -> Automatisierte Cloud-Bildungseinrichtung von AWS Batch Pipelines: Das ist großartig

Pakete

• Mlmax – Schnelle Bibliothek starten
• Kleinigkeit
• Pyutil

Allgemein

• Deep-Learning-Container
• Loguru -> Protokollierungsbibliothek
• AWS GDAL Robot -> Lambda und Stapelverarbeitung von Geotiffs
• Serverlose seismische Verarbeitung
• LIthops -> Multi-Cloud-Framework für verteiltes Computing

Übersichten

• Mineralienexploration

Domänen

• Geologie
  • Geologische Zeitalter
  • Lithologie
  • Stratigraphie
• Geochemie
• Geophysik
• Fernerkundung

Webdienste

Bei der Auflistung wird davon ausgegangen, dass es sich im Allgemeinen um Daten handelt. Bei nur Bildern wie WMS wird dies angegeben.

Welt

• Kritische Mineralien und Vorkommen

Australien

• AusGIN
• Geowissenschaften Australien
• Mineralisches Potenzial -> WMS
• Geoscience Australia Katalogservice

Geologie

• AUSLAMP - > Tennant Creek - MtIsa
• Feldgeologie
• Tiefe Lithosphäre -> Mineralpotenzial der tiefen Lithosphäre
• Geochronologie -> Geochronologie
• Geologische Provinzen
• WMS -> WMS-Bild
• EGGS -> Schätzungen geologischer und geophysikalischer Oberflächen
• Proterozoische alkalische Gesteine ​​– Proterozoische alkalische Gesteine, Datensatz WFS {hat auch WMS}
• Känozoikum
• Mesozoikum
• Paläozoikum
• Archaisch
• Stratigraphie -> Stratigraphische Einheiten

Geophysik

• Geophysikalische Untersuchungen
• Seismische Untersuchungen -> Seismische Untersuchungen an Land
• Magnetotellurik -> Nordaustralien AUSLAMP-Stationen

Andere

• Ni-Cu-PEGE -> Intrusion beherbergte Nickel-Kupfer-PGE-Lagerstätten
• EFTF-Gebiet -> Erkundung der zukünftigen Gebiete
• Temperatur -> Interpretierte Temperatur
• DEA -> Digital Earth Australia
• Landbedeckung
• Gewässer
• Stückliste -> Büro für Meteorologie und Hydrogeochemie

New South Wales

• NSW
• WCS
• WFS-Mineralbohrlöcher
• WFS Erdölbohrlöcher
• WFS Kohlebohrlöcher
• Seismisch -> Seismisch und andere

Queensland

• Queensland
• Geowissenschaftlich -> Geophysik- und Berichtsindex
• Geologie
• Regional
• Zustand
• Mietshäuser
• Straßen
• Wasserlauf

Südaustralien

• SARIG
• Bohrlöcher
• Geologie
• Geophysik
• Perspektive
• Mineralien und Minen
• Fernerkundung
• Seismisch
• Mietshäuser

Northern Territory

• NTGS -> Northern Territory Geological Survey

Tasmanien

• Tasmanien WFS
• Tasmanien REST
• Bohrlöcher

Victoria

• Victoria Geonetwork

Westaustralien

• Westaustralien
• Ausruhen

Neuseeland

• GNS -> Liste der Webdienste

Südamerika

Brasilien

• Geoportal Brasilien
• Brasilien CPRM

Peru

• Ingement
• Mineralische Vorkommen
• Umweltfreundlich

Mexiko

• GeoInfo -> Restdienste

Argentinien

• SIGAM

Kolumbien

• Ausruhen
• Einzahlungen 2018

Uruguay

• Ausruhen

Andere

• SIG Andes -> Geologie der Anden

Europa

EGDI -> EGDI-Mineralien

Schweden

• SGU Magnetics WMS
• SGU Uran
• Geophysik-Metadaten

Finnland

• GTK -> Geologischer Dienst Finnlands
  • Finnland
    • Grundgesteinsgeologie
    • Geophysik
    • Bodenvermessungen
• Arktische Mineralien -> Arktische 1M Mineralvorkommen

Dänemark

• deus -> Grönland WMS/WFS

Portugal

• Portugals Geologie
• Mineralvorkommen -> WMS
• Städte und Gemeinden

Spanien

• Spanien
• Geologie -> 200K
• 1M -> 1M
• 50.000 -> 50.000
• IGME-Geode
• Geophysik
• Kupfer - Kupfer
• GeoFPI - > Geologie und Mineralien Südportugiesische Zone
• Wasser

Ukraine

• Geoinform -> [derzeit ausgesetzt]

Irland

• Ausruhen
• Mineralstandorte

Großbritannien

• BGS -> British Geological Survey
• Geoindex -> Beispiel für Mineralvorkommen
• Rest -> BGS Rest Services & Inspire 625

Deutschland

• BRG

Tschechische Republik

• Ausruhen

Slowakei

• Ausruhen

Ungarn

• MBFZ OGC
• MBFZ Rest

Rumänien

• Nur IGR -> WMS
• IGR-Minen -> nur WMS

Polen

• Restbeispiel -> Viele weitere Mapserver

Nordamerika

Kanada

• Québec
• NWT
• Ausruhen
• Referenzen

USA

• USGS World Mineral
• USGS MRDS
• Minnesota

Asien

• China -> WMS-Minerallagerstätten-Wap
• Erzfeld -> Mineralvorkommenspunkte
• India Mineral -> WMS
• Indien Geophysik

Afrika

• Afrika-Geoportal -> Rastdienste
• Afrika 10M -> Afrika 10M Mineralvorkommen https://pubs.usgs.gov/of/2005/1294/e/OF05-1294-E.pdf
• IPIS Artisanal Mines -> Es gibt auch eine WMS-Version
• Github
• Uganda -> GMIS WMS

Allgemein

• Mineral Exploration Web Services -> QGIS-Plugin mit Zugriff auf viele relevante Webdienste

Andere

• Straßenkarte öffnen -> nützlicher allgemeiner Kacheldienst

APIs

• Open Data API -> GSQ Open Data Portal API
  • Geochemische Analyse
• CORE -> Offene Forschungstexte
  • API Notebook -> Beispiel und Funktionen
• TEILEN -> Open Science API
• USGS-Veröffentlichungen
• KREUZVERWEIS
• xDD -> ehemaliges GeoDeepDive
• ADEPT -> GUI zu xDD zum Durchsuchen von 15 Millionen gesammelten Papieren
• OpenAlex
  • API
  • Diophila-Python-Bibliothek
  • Python-Bibliothek
    • Papier
• Makrostrat
  • Papier
• OpenMinData -> erleichtert das Abfragen und Abrufen von Daten zu Mineralien und Geomaterialien über die Mindat-API

Datenportale

Welt

• Zusammenarbeit mit Erdmodellen -> Zugriff auf verschiedene Erdmodelle, Visualisierungstools für die Modellvorschau, Möglichkeiten zum Extrahieren von Modelldaten/Metadaten und Zugriff auf die beigesteuerte Verarbeitungssoftware und Skripte.
• ISC-Bulletin -> Suche nach Erdbebenherdmechanismen
• [Magnetics Information Consortium[(https://www2.earthref.org/MagIC/search) -> paläomagnetisch, geomagnetisch, gesteinsmagnetisch

Australien

Geowissenschaften Australien

• Geoscience Australia-Datenkatalog
• AusAEM
• Geoscience Australia-Portal
• Portal „Exploring for the Future“ -> Geoscience Australia-Webportal mit Download-Informationen
• AusAEM
• AusLAMP
• Geochronologie und Isotope
• Hydrogeologie-Einzugsgebiete -> Suche nach der Ebene „Einzugsgebiete“.
• Initiative zur Kartierung kritischer Mineralien
• Australische stratigraphische Einheiten
• Stratigraphische Einheiten des australischen Bohrlochs -> Zusammenstellung für Grundwasser von Sedimenteinheiten
• Geoscience Australia Geophysics-Thredds -> OpendDAP- und https-Zugriff
• MORPH gdb -> Bohrdaten von Officer Musgrave

CSIRO

• CSIRO-Datenzugriffsportal
• Regolithtiefe
• TWI -> Topografischer Nässeindex
• ASTER Geoscience Maps -> Website
  • FTP -> CSIRO-FTP-Site
  • ASTER Maps-Notizen -> Notizen zu den oben genannten Punkten

AuScope

• 3D-Geologie -> Modelle aus mehreren Bereichen

Seeschwalbe

• Verbesserte Kovariaten der nackten Erde für die Boden- und lithologische Modellierung

Büro für Meteorologie

• Grundwasserforscher -> Büro für Meteorologie

Grundlegende räumliche Daten

• Elvis

Südaustralien

• SARIG -> Geodatenkartenbasierte Suche des South Australia Geological Survey
• SARIG-Katalog -> Datenkatalog
  • 3D-Modelle
    • SA Breit
    • s3-Standort
  • Datenpakete – Jährliche Aktualisierung
  • s3-Berichte -> Berichte und Textversionen im S3-Bucket mit Webschnittstelle)
  • Berichte
  • Seismisch
    • Seismische Downloads -> Eine Seite mit Links

Northern Territory

• STRIKE -> Northern Territory Geological Survey
• GEMIS
  • McArthur-Becken -> 3D-Modell
  • Geophysikalische Untersuchungen
  • Geophysik -> Referenz
  • Bohren und Geochemie -> Referenz
    • Datenpaket -> Datenpaket

Queensland

• Geologische Untersuchung von Queensland
• Geophysikalische Untersuchungen
• Bohren und Geochemie

Westaustralien

• GEOVIEW -> Western Australia Geological Survey
• DMIRS -> DMIRS Daten- und Softwarezentrum
• Download-URLs -> Datensatz der Download-Links
• Bohren und Geochemie
• Paket herunterladen - Verbesserung?
• Geochemie
• Erdölbrunnen mit Tiefen
• Daten-WA-Teilmenge

NSW

• MINVIEW -> New South Wales Geological Survey
• DiGS -> Publikationen und Geotechnische Sammlungen

Tasmanien

• MRT
• MRT-Karten -> Webkarte

Victoria

• Erdressourcen
  • Geophysik
• GeoVIC -> Webmaps erfordert eine Registrierung, um nützlicher zu sein

Neuseeland

• Explorationsdatenbank -> Online
• GERM -> Geologische Ressourcenkarte von Neuseeland
• Geologie -> Webkarte
  • https://maps.gns.cri.nz/gns/wfs

Südamerika

Brasilien

• CPRM -> Geologische Untersuchung Brasiliens
• Downloads -> Downloads des Brazil Geological Survey
• Rigeo -> Institutionelles Repository für Geowissenschaften

Peru

• Ingemmet GeoPROMINE -> Geologische Untersuchung von Peru
• GeoMAPE

Mexiko

• GeoInfo

Argentinien

• SIGAM -> Argentinischer geologischer Dienst
• SIGAM

Kolumbien

• Geportal

Uruguay

• Schaden

Chile

• Portalgeomin

Europa

• EGDI -> Europa Geowissenschaften
• WFS
• Prominieren
• Inspirieren -> Geoportal inspirieren

Dänemark

• Dänische Untergrunddaten

Finnland

• Mineralien4EU
• GTK -> Geologischer Dienst Finnlands
• Geochemische Karten -> nur PDF!

Schweden

• SGU -> Schwedischer Geologischer Dienst

Spanien

• IGME -> Spanischer geologischer Dienst

Portugal

• Geoportal
• Mineralvorkommen

Irland

• GSI -> Geological Survey of Ireland
• GSI – Kartenbetrachter
• Goldmine -> Karten- und Dokumentensuche
• data.gov.ie -> Nationale Portalansicht
• isde -> Irish Spatial Data Exchange

Norwegen

• NGU -> Geologische Untersuchung Norwegens
• Datenbank -> Nachschlagen von Mineralressourcen und Stratigraphie
• Github
• API
• Geoporta -> Geophysik
• GEONORGE -> Datenkatalog mit Download

Großbritannien

• Großbritannien
• Kartenserver
• Github

Ukraine

• Bodenschätze

Russland

• Russisches Geologisches Forschungsinstitut -> Derzeit nicht zugänglich
• RGU -> GIS-Projekt der Lagerstätten

Deutschland

• Geoportal
• Geokarte -> M
• Atom -> Atom-Datenfeed
• GDI -> 3D-Modelle Deutschland

Frankreich

• Infoterre -> Französischer geologischer Dienst

Kroatien

• Geoportal -> Geologische Untersuchung Kroatiens
• Geologie

Tschechische Republik

• GS -> Tschechischer Geologischer Dienst

Slowenien

• Datenkatalog

Slowakei

• Datenkatalog
• Geoportapi-API

Ungarn

• MBFSZ-Karten

Rumänien

• IGR -> Rumänischer Geologischer Dienst
• Bodenschätze

Polen

• Geoportal

Vereinigtes Königreich

• Britische Onshore-Geophysikalische Bibliothek
• OS Data Hub Britische Geologie
• Geologie 625

Nordamerika

Kanada

• Natürliche Ressourcen Kanada
• Github
• Geowissenschaftliches Datenrepository -> DAP-Server
• Bergbau-Webkartenportal
• DEM -> Kanada DEM im COG-Format
• CDEM -> Digitales Höhenmodell (2011)
• Ontario
• Québec
• SIGEOM-Datenbank
• Britisch-Kolumbien
• Datenbank zum Vorkommen von Mineralien
• Yukon
  • ftp
• Neuschottland
• provinziell
• Prinz-Edward-Insel
• Saskatchewan
• Datenbank mit Mineralvorkommen
• Neufundland -> hat in Chrome nicht funktioniert, habe es in Edge versucht
• Alberta
• Interaktive Kartenanwendung
• Nordwest-Territorien
• Mineralbesitz

USA

• USGS -> Kartendatenbank
• MRDS -> Mineralressourcen-Datensysteme
• Earth Explorer -> USGS-Fernerkundungsdatenportal
• Nationale Kartendatenbank
• Nationale Kartendatenbank
• Alaska
• ReSci -> Register der wissenschaftlichen Sammlungen des National Geological and Geophysical Data Preservation Program
• Michigan

Afrika

• Kataster
• Hydrogeologie -> Hydrogeologie und Geologie aus dem Grundwasseratlas
• Westafrika -> Mineralvorkommen
• Namibia
• Mineralische Vorkommen
• Bergleute
• Südafrika -> Geologische Untersuchung Südafrikas
• Mineralvorkommen -> Beispiel, bei dem Sie sich zum Herunterladen anmelden müssen
• Uganda -> GMIS-Portal
• Metallische Mineralien
• Tansania
• Mineralische Vorkommen
• Minen
• SIGM -> Tunesien Geologie und Bergbau
• Sambia -> Hier finden Sie Mietwohnungen in Sambia

Asien

China

• Geowissenschaftliche Daten
• Mineralische Vorkommen
• Nationale Datenbank für Mineralvorkommen

Indien

• Bhukosh -> India Geological Survey
• Beachten Sie, dass die Geologie in Rajasthan nur in Stücken funktioniert, was schmerzhaft ist – wenn Sie es möchten, lassen Sie es mich wissen

Saudi-Arabien

• Nationales geologisches Datenbankportal

Andere

Geologie

• StratDB
• GEM Globale aktive Fehler
• RRuff-Mineraleigenschaften
• Artikel -> Evolutionäres System der Mineralogie
• OneGeology
• Katalog

Iran

Geologie

• OGC BGS 1M Grundgestein

Allgemein

• OSF -> Open Science Foundation
• Sedimentgehostete Basismetalle -> Sedimentgehostete Basismetalle
• Lithosphären-Athenosphären-Grenze -> LAB Hoggard/Czarnota
• Liste der geologischen Untersuchungen

Berichte

Australien

• Northern Territory GEMIS
  • suchen
  • Beispiel
• Südaustralien SARIG
• Westaustralien WAMEX
  • suchen
  • API
    • Beispiel
• Queensland
• NSW-Ausgrabungen
  • NSW Digs geöffnet
  • API nicht öffentlich
• PorterGEO -> Datenbanken zu weltweiten Mineralvorkommen mit zusammenfassenden Übersichten
• Sustainable Minerals Institute -> Organisation universitätsnaher Forscher in Queensland, die Datensätze und Wissen erstellen

Kanada

• Britisch-Kolumbien
  • Suche -> Mineralbewertungsberichte
  • Veröffentlichungen -> Veröffentlichungen
• Ontario -> Mineralbewertungsberichte
  • Ontario-Veröffentlichungen
• Alberta
  • Veröffentlichungen
• Yukon
• Fußabdruck
• Manitoba
• Veröffentlichungen
• Neufundland und Labrador
• Nordwest-Territorien
• Neuschottland
• Québec
• Saskatchewan
  • Suchen
  • iMaQs -> Integriertes Bergbau- und Steinbruchsystem

USA

• Arizona
• Montana
• Nevada
• New Mexico
• Minnesota
• Michigan
• json
• Alaska
• Washington

Andere

• British Geological Survey NERC
  • Mineralisches Potenzial
  • Suchen
  • API-Beispiel
  • Veröffentlichungen
  • MEIGA -> MEIGA 600 BGS Mineralexplorationsprojektberichte
• GeoLagret -> Schweden
• MinData -> Zusammenstellung von Gesteinsstandorten aus aller Welt
• Mineraliendatenbank -> Exportierbare Liste von Mineralien mit wissenschaftlichen Eigenschaften und Alter
• NASA
• ResearchGate -> Forscher- und Berufsnetzwerk

Werkzeuge

GIS

• QGIS -> GIS-Datenvisualisierung und -analyse Open-Source-Desktopanwendung, verfügt über einige ML-Tools: Unverzichtbar für eine schnelle und einfache Anzeige
  • 2d Geologie in QGIS -> Workshop für QGIS NA 2020 Einführung geologischer Karten und Querschnitte für Studenten und Hobbyisten
  • OpenLog -> Bohrloch -Plugin Beta
  • Geo -SAM -> QGIS -Plugin für Segment irgendetwas mit Raster
  • Evidenzgewichte
  • Plugin
• GRAS
• Saga -> Spiegel von Sourceforge

3D

• Pyvista -> VTK -Wraping -API für eine großartige Datenvisualisierung und -analyse

  • Pvgeo
  • Pyvista -Xarray -> Xarray -Daten in VTK 3D schmerzlos: Eine großartige Bibliothek!
  • Omfvista -> Pyvista für das offene Bergbauformat
  • Scipy 2022 Tutorial

• PymeshLab -> Netzumwandlung

• Offenes Bergbauformat

• Whitebox -Werkzeuge

  • GUI -> Desktop -Version

• Untergrund

• Geolambda -> AWS Lambda Setup

• Geowissenschaftsanalyst

  • GeOH5PY -> Daten an und von GEOH5 -Projekten abrufen
  • GeoApps -> Notebook -basierte Anwendungen für Geophysik über GeoH5Py
  • Geoh5vista
  • GAMS -> Magnetische Datenanalyse
    • Papier - Ein Rahmen für Mineralgeowissenschaftsdaten und Modelltretabilität - GeOH5

• Rayshader

• Vdeo

Geospatial General

• Python -Ressourcen für die Erdwissenschaft
• Geoutils -> Geospatial Analysis und Foster -Interoperabilität zwischen anderen Python -GIS -Paketen.

Vektordaten

Python

• Geopandas
  • Dask-Geopandas
    • Anleitung
  • GeoFileops -> Räumliche Verbindungen der Geschwindigkeit über Datenbankfunktionen und Geopackage
• Kart -> Verteilte Versionskontrolle für DAATA
• Pyesridump -> Bibliothek, um Daten im Maßstab von Esri Restservern zu greifen

R

• SF
• Terra -> Terra bietet Methoden zur Manipulation geografischer (räumlicher) Daten in "Raster" und "Vektor".

Rasterdaten

C

• exactextract -> Befehlszeile Zonale Statistiken in C.

Julia

• Rasters.jl -> Lesen und Schreiben gemeinsamer Rasterdatentypen

Python

• Rasterio -> Python -Basisbibliothek für Rasterdatenbearbeitung
• GeOreader -> Rasterdaten aus verschiedenen Satellitenmissionen verarbeiten
• RasterStats -> Zusammenfassende Geospatial -Raster -Datensätze basierend auf Vektorgeometrien
• Xarray -> Mehrdimensionale markierte Array -Handhabung und -analyse
  • Rioxarray -> fabelhafte API auf hoher Ebene für die Bearbeitung von Rasterdaten mit Xarray
  • Geocube -> Rasterisierung der Vektordaten -API
  • ODC -GEO -> Werkzeuge für Rasternbasis mit Fernerkundungen mit vielen extrem praktischen Werkzeugen wie Farbpolizei, Gitter -Workflows
  • COG Validator -> Format von Cloud -optimierten Geotiffs überprüfen
  • serverless-datacube-Demo-> Xarray über Lithops / Coiled / Modal
  • Xarray Spatial -> Statistische Analyse von Rasterdaten wie Klassifizierung wie natürliche Pausen
  • XDGGS -> Andere Arten von Netzen
  • XGCM -> Histogramme mit Etiketten
  • XRFT -> Xarray -basierte Fourier -Transformationen
  • XVEC -> Vektordatenwürfel für Xarray
  • Xarray -Einstats -> Statistiken, lineare Algebra und Einops für Xarray

R

• Raster -> R -Bibliothek
• Terra -> bietet Methoden zur Manipulation geografischer (räumlicher) Daten in "Raster" und "Vektor".
• Sterne -> räumlich -zeitliche Arrays: Raster- und Vektordatacubes
• exaktextracr -> Raster -Zonale Statistik für r

Benchmarks

• Raster -Benchmark -> Benchmarking Einige Raster -Libaries in Python und R.

Gui

• WhiteBox Tools -> Python API, GUI usw. haben für die Berechnung des topografischen Nässeindex verwendet

Datenerfassung

• Piautostage-> 'Ein Open-Source-3D-gedruckter Tool für die automatische Sammlung hochauflösender Mikroskopbilder;' für Mineralproben entwickelt.

Datenkonvertierung

• AEM zu Seg-y
• ASEG GDF2
• CGG Outfile Reader
• Geosoft Grid an Raster
• Loop Geosoft Grid
• Mundharmonica Geosoft Grid -> Anfrage in Fortschritt bei der Konvertierung in Xarray
• Auscope -> Daten aus binären Gocad -Modellen
• Gocad SG Grid Reader
  • Geomodel-2-3dweb-> Hier haben sie eine Methode, um Daten aus binären GOCAD-SG-Gittern zu extrahieren
• Leser von Leapfrog Mesh
• OMF -> Open Mining -Format für die Konvertierung zwischen Dingen
• PDF Miner
• VTK zu DXF

Geochemie

• Pygeochemtools -> Bibliothek und Befehlszeile, um schnelle QC und die Darstellung geochemischer Daten zu aktivieren
• SA Geochemische Karten -> Datenanalyse und Darstellung von Geochemie -Daten in Südaustralien aus der Geological Survey of SA
• Geochemisches Levening
• Scott Halleys Geochemie -Tutorial
• Periodenzüchter

Geostatistik

• Geostatspy
• Pyinterpolat

Geochronologie

• Geologische Zeitskala -> Code zum Erstellen, hat aber auch ein schönes reguläres CSV der Altersgruppen

Geologie

• Gempy -> Implizite Modellierung

• Edelsteinhilfe

• Schleifenstruktur -> Implitätsmodellierung

• Handbuch Python Geologia -> Analyse der Geologiedaten

• MAP2LOOP -> 3D -Modellierungsautomatisierung

  • Loop3d -> GUI für MAP2LOOP

• PybedForms

• SA Stratigraphy -> Stratigraphy Database Editor WebApp

• Striplog

• Analise_de_dados_estruturais_altamira

• Globale Tektonik -> Open Source -Datensatz zum Aufbau, Platten, Ränder usw.

  • Papier

• Zenodo -Ergänzungen

• Litholog

• Pygplates

• Tutorialdaten

  • Papier

Geophysik

• GEOSCIENCEIENce Australia Utilities
• Geophysik für das Praktizieren von Geowissenschaftlern
• Potenzielle Feld -Toolbox -> Einige Xarray -basierte schnelle Fourier -Transformationsfilter - Derivate, Pseudogravität, RPG usw.
  • Notebook -> Klasse mit einigen Beispielen [vertikaler Derivat, Pseudogravität, Aufwärtsdauer usw.)
• Berechnung Geophysik Sandbox
• RIS -Kellersediment -> Tiefe zum magnetischen Keller in der Antarktis
• Signalbildverarbeitung

Elektromagnetisch

• Geoscience Australia AEM
• UH Electromagnetics -> Kursarbeitsnotizen zum Verständnis dieser Domäne
• AEM -Interpretation
• Emag PY -> FDEM
• Resipie -> DC / IP

Schwerkraft und Magnetik

• Mundharmonika
• Filterbeispiele -> Fast Fourier Transform -basierte Verarbeitung über Xarray
• Australische Schwerkraftdaten
• Würmer
• Worms Update <- potenzielle Felder Wurmerstellung mit einigen geringfügigen Updates, um neue Networkx-API *Github Mirror zu verarbeiten
• Osborne Magnetic -> Umfragendatenverarbeitungsbeispiel

Seismisch

• Segyio
• Segysak -> Xarray -basierte Seg -y -Datenhandhabung und -analyse
• Geophysikalische Notizen -> Seismische Datenverarbeitung

Magnetotellurik

• MTPY
• Tutorials
• MTPY -> Aktualisierung des oben genannten, um die Dinge einfacher zu machen
• Mineralstatistik -Toolkit -> Abstand zur MT -Merkmalsanalyse
• Lithosphärische Leiterpapier
• MTWAFFLE -> MT -Datenanalyse Beispiele
• Pymt
• Widerstand
• Mekmus -> Werkzeuge zum Lesen des elektrischen Leitfähigkeitsmodells der USA
• Modell

Gitter

• GMT
  • Pygmt
• Verde
• Grid_aeromag -> brasilianisches Gitterbeispiel
• Pyinterp -> Mehrdimensionales Gitter über Boost
• Pseudogravität -> von Blakely, 95

Umkehrung

• Simpeg
  • Mira Geoscience Fork -> für GeoApps verwendet
  • Simpeg Fork
  • Transformation 2020 Simpeg
  • Transformation 2021 Simpeg
  • Simpeg -Skripte
  • Beispiel für Astic -Gelenkinversion
• Gimli
• Tomofast-X
• USGS Anonymous FTP
• USGS -Software -> längere Liste älterer nützlicher Dinge: Dosbox, jemand?
• Geophysik -Unterroutinen -> FORTRAN CODE
• 2020 Aachen -Inversionsprobleme -> Überblick über die Gravity -Inversionstheorie

Geochemie

• Pyrolit
  • GS2021 Pyrolit
• Nivellierung
• Pygeochem -Werkzeuge
• Geoquimica
• Geochemistrypi
  • Papier

Bohren

• DH2LOOP -> Bohrintervallhilfe
  • Papier
• Drilldown -> Bohrvisualisierung in Notebooks über GeOH5PY -> Hinweis DeSurveying
• Pygslib -> Downhole -Vermessung und Intervallnormalisierung
• Pyborehole -> Verarbeitung und Visualisierung von Bohrlochdaten
• DHComp -> Komposites Geophysikalische Daten zu einer Reihe von Intervallen

Fernerkundung

• Fantastische Spektralindizes -> Anleitung zur Spektralindexerstellung
• Datenwürfel öffnen
  • DEA -Notizbücher -> Code für die Verwendung in Workflows im ODC -Stil
  • Datacube -Stats -> Statistische Analysebibliothek für ODC
  • GEO -Notizbücher -> Code Beispiele aus Element 84
• Raster4ML -> Eine große Anzahl von Vegetationsindizes
• Lefa -> Frakturanalyse, Abstammungslinien

Serverlos

• Kerchunk -> serverloser Zugriff auf Cloud -basierte Daten über Zarr
  • Kerchunk GeOH5 -> Zugriff auf Geowissenschaftsanalysten/GeOH5 -Projekte serverlos über Kerchunk
  • ICEHUNK -> Transaktionsspeicher -Engine für Tensor / ND -Array -Daten, die für die Verwendung im Cloud -Objektspeicher ausgelegt sind.

STAC -Kataloge

• DEA StackStac -> Beispiele für die Arbeit mit Digital Earth Australia Data
• Einnahme-Stac
• ML AOI -Erweiterung
• ML -Modellerweiterungsspezifikation -> Modellspezifikation für maschinelle Lernmodell für Katalogingspatio -zeitliche Modelle
  • Papier
• ODC -STAC -> Datenbank freien offenen Datenwürfel
• Pystac
• Sa-Suche
• StackStac -> Metadaten beschleunigten Dask- und Xarray -Timeseries

Statistiken

• Orange -> Data Mining GUI
• Hdstats -> Algorithmische Grundlage geometrischer Mediane
• Hdmedians

Visualisierung

• TV -> Satellitenbilder in einem Terminal anzeigen
• Anstritt
• Sitzt
• Hsdar
• Sterne
• Peru Gold Mining SAR

Mineralpotential

• Nickel Mineralpotential Mapping -> ESRI -basierte Analyse
• Prospektivität Online -Tool

Bergbauökonomie

• Bluecap -> Framework von der Monash University zur Bewertung der Lebensfähigkeit der Bergungen
• ZiPFS -Gesetz -> Kurvenanpassung der Verteilung von Mineralabliedern
• Pyasx -> ASX Data Feed Scraping
• Metallpreis -API -> Containerisierte Microservice

Visualisierung

• Napari -> Mehrdimensionaler Bild Betrachter
• Holoviews -> Datenvisualisierung von großem Maßstab
• GraphViz -> Diagramm -Plot-/Anzeigeunterstützungsinstallationsinformationen
• Räumlich-kde

Colormaps

• CET wahrnehmungsgemäß einheitlich Colormaps
• PU COLORMAPS -> Für den Benutzer im Geowissenschaftsanalyst formatiert
• Colormap -Verzerrungen -> eine Panel -App, die Verzerrungen durch nicht -konpertenonische Kolormaps auf geophysikalischen Daten erstellt hat
• Rissdaten aus Colormpas
• Öffnen Sie geowissenschaftliche Codeprojekte

Geodaten

• Geospatial>- Installiert mehrere gemeinsame Python-Pakete
• Geospatial Python -> kuratierte Liste

Technologiestapel

C

• GDAL -> Absolut entscheidender Datenumwandlungs- und Analyse -Framework
  • Tools -> Hinweis enthält viele Befehlszeilen -Tools, die ebenfalls sehr nützlich sind

Julia

• Julia Erde -> Förderung der Geospatial Data Science und geostatistische Modellierung in der Erdwissenschaften
• Julia Geodynamics -> Computational Geodynamics Code
• Einführung in Julia für Geowissenschaften

Python - Pydata

• ANACONDA -> LOTS bereits mit diesem Paketmanager installiert.
  • Gdal et al.
  • Git Bash -> Conda dazu bringen, in Git Bash zu arbeiten
• Numpy mehrdimensionale Arrays
• PANDAS TABULUS -Datenanalyse
• Matplotlib -Visualisierung
• Zarr -> komprimierte, verteilte verteilte Array
• Dask -> Parallele, verteiltes Computing
  • Dask Cloud Anbieter -> Starten Sie die Cluster von Dask -Clustern automatisch auf der Cloud
  • Dask Median -> Notebook, das einen Dask Median -Funktionsprototyp gibt
• Python Geospatial -Ökosystem -> kuratierte Informationen

Rost - Georust

• Georust -> Sammlung von Geospatial -Versorgungsunternehmen im Rost

Datenbanken

• Duckdb -> In Process OLAP DB mit Geschwindigkeit - verfügt über einige Geospatial- und Array -Funktionen
• ibis + duckdb geopsatial -> scipy2024 talk

Datenwissenschaft

• Python Data Science Vorlage -> Projektpaket Setup
• Awesome Python Data Science -> kuratierter Leitfaden

Wahrscheinlichkeit

• Distfit -> Wahrscheinlichkeitsdichteanpassung

Wissenschaft

• Python -Ressourcen für Erdwissenschaften
• Fantastisches wissenschaftliches Computer

Docker

• AWS Deep Learning Container
• Räumlicher Docker
• DL Docker Geospatial
• Rocker
• Docker Lambda
• Geobase
• DL Docker Geospatial

Ontologien

• Geologische Gesellschaft des Queensland -Vokabulars
  • Geologische Eigenschaftendatenbank
  • Geofeatures
• Geologische Gesellschaft von Westaustralien
• Stratigraphisch
• Geowissenschaftlicher Wissensmanager
• Geosciml -Vokabeln

Bücher

Python

• Python Geospatial Analysis Kochbuch
• Geoprocessing mit Python -> Manning LiveBook

Andere

• Lehrbuch
• Maschinelles Lernen in der Öl- und Gasindustrie
• Geokomputation mit r
• Earthdata Cloud -Kochbuch -> So greifen Sie auf NASA -Ressourcen zu
• Kochbuch des Datenreinigers -> UNIX -Tools für die Verwendung von Daten zum Streben und Reinigen einsetzen
• Enzyklopädie der mathematischen Geowissenschaften
• Handbuch der mathematischen Geowissenschaften

Andere

• GXPY -> Geosoft Python API
• Eartharxiv -> Papiere aus dem Preprint -Archiv herunterladen
• Essoar -> Preprint Papierarchiv

Datensätze

Welt

Geologie

• Grundgestein -> Generalisierte Geologie der Welt
• Glim -> Globale Lithologiekarte
• Paläogeologie Ein Atlas von phanerozoischen paläogeografischen Karten
• Sedimentschichten -> Globale 1 -km -Netzdicke von Boden-, Regolith- und Sedimentablagerungsschichten
• Weltstresskarte -> Globale Zusammenstellung von Informationen über das heutige Stressfeld der Kruste
• GMBA -> Global Mountain Inventory

Geophysik

Schwerkraft

• Krümmung -> Globale Krümmungsanalyse aus Schwerkraftgradientendaten
• WGM 2012

Magnetik

• EAMG2V3 _> Erdmagnetanomalie -Gitter
• WDMAM -> Welt Digital Magnetic Anomalie Map

Magnetotellurik

• EMC -> Globales 3D -inverse Modell der elektrischen Leitfähigkeit

Seismisch

• Labor slnaafsa
• Labor CAM2016
• Moho -> Gemma -Daten
• Moho -> Szwillus Daten
• Seismische Geschwindigkeit -> Debayle et al.
• Lithoref18 -> Ein globales Referenzmodell der Lithosphäre und des oberen Mantels durch gemeinsame Inversion und Analyse mehrerer Datensätze
• Crust1.0 -> Globales Krustenmodell netCDF
• Übersicht Homepage

Thermal

• Wärmefluss

Allgemein

• Tiefe Zeit Digital Earth -> Daten und Visualisierung für eine Vielzahl von Datenquellen und Modellen
• Earthchem -> Community -gesteuerte Erhaltung, Entdeckung, Zugang und Visualisierung geochemischer, geochronologischer und petologischer Daten
• Georoc -> Geochemische Zusammensetzung von Gesteinen
• Globale Geologie -> Ein kurzes Rezept zur Erstellung einer globalen Geologiekarte im GIS -Format (z.
• Große Igenous Provinc Commission
• Mantelfahnen
• Sedimentdicke -> Karte
• SpatialReference.org -> Repository für die Website

Australien

• Gemeinsames Erdmodell
• Schwere Mineralkarte
• Schwere Mineralkarte des australischen Piloten
• Glänzende App

Geochemie

• Prädiktive Gitter der Hauptoxidkonzentrationen in Oberflächengestein und Regolith über dem australischen Kontinent -> verschiedene Oxide

Geologie

• Alkalische Gesteine ​​Atlas
• Zenozoisch
• Mesozoikum
• Paläozoikum
• Archaer
• suchen
• Proterozoische alkalische Gesteine ​​-> Proterozoikum alkalisch und verwandte magmatische Gesteine ​​von Australien GIS
• Zenozoisch
• Mesozoikum
• Paläozoikum
• Archaer
  • Papier https://ecat.ga.gov.au/geonetwork/srv/eng/catalog.search#/metadata/147963
• Hydrogeologie -> Hydrogeologiekarte Australiens
• Hydrogeologie -> 5m
• Layered Geology -> 1m
• Oberflächengeologie -> 1 m Skala
• Der australische Mafic-Ultramafic Magmatic Events GIS-Datensatz

Geophysik

• Schwerkraft -> 2019 Australische nationale Schwerkraftgitter

Magnetik

• TMI -> Magnetische Anomaliekarte von Australien, siebte Ausgabe, 2019 TMI
• 40m -> 40m Version
• VRTP -> GRID TOTAL MAGNETIGNISSIGKEIT (TMI) GRID Australiens mit variabler Reduktion auf Pole (VRTP) 2019
• 1VD -> Gesamtmagnetintensitätsnetz von Australien 2019 - Erster vertikaler Derivat (1VD)

Radiometrie

• Radiometrie -> Komplettes radiometrisches Gitter von Australien (RadMAP) V4 2019 mit modellierter Infill
• K -> radiometrisches Gitter von Australien (RADMAP) V4 2019 gefilterte PCT -Kaliumnetze
• U -> radiometrisches Netz von Australien (Radmap) V4 2019 gefilterte PPM -Uranium
• TH -> Radiometrisches Gitter von Australien (Radmap) V4 2019 gefilterte ppm Thorium
• TH/K -> Radiometrisches Netz von Australien (Radmap) V4 2019 -Verhältnis Thorium über Kalium
• U/K -> Radiometrisches Gitter von Australien (Radmap) V4 2019 -Verhältnis Uran über Kalium
• U/TH -> Radiometrisches Netz von Australien (Radmap) V4 2019 -Verhältnis Uran über Thorium
• U Squared/Th -> Radiometrisches Netz von Australien (Radmap) V4 2019 -Verhältnis Uran quadratisch über Thorium
• Dosisrate-> radiometrisches Gitter von Australien (Radmap) V4 2019 gefilterte terrestrische Dosisrate
• Ternäres Bild -> radiometrisches Gitter von Australien (Radmap) V4 2019 - Ternäres Bild (K, Th, U)

Ausaem

• AUSAEM 1 -> AUSAEM Jahr 1 NT/Qld Airborne Electromagnetic Survey; GA Layered Earth Inversion Produkte
• AUSAEM 1 -> AUSAEM Jahr 1 NT/QLD: Tempest® Airborne Electromagnetische Daten und EM Flow® -Leitfähigkeitsschätzungen
• AUSAEM 1 -> AUSAEM1 Interpretationsdatenpaket
• Ausaem 2 -> Ausaem 02 WA/NT 2019-20 Airborne Electromagnetic Survey
• Ausaem -Wa -> Ausaem -WA, Murchison Airborne Electromagnetic Survey Blöcke
• Ausaem-Wa-> Ausaem-Wa, Southwest-Albany Airborne Electromagnetic Survey Blocks
• Ausaem -Wa -> Ausaem WA 2020-21, Ostgoldfields & Ost Yilgarn Airborne
• Ausaem -Wa -> Ausaem (WA) 2020-21, Earaheedy & Desert Strip
• Ausaem ERC -> Ausaem Eastern Resources Corridor
• Ausaem WRC -> Ausaem Western Resources Korridor
• Interp -Übersicht
• Nationale Leitfähigkeitsnetze der Oberfläche und Nahen Oberfläche -> Nationale ML -Interpolation für Ausem in ähnlicher Weise wie Nordaustralien

Auslamp

• Auslamp Sea -> Widerstandsmodell des südostaustralischen Festlandes aus Auslamp Magnetotelluric -Daten
• Victoria -Daten
• NSW -Daten
• Auslamp Tisa -> Widerstandsmodell, das aus Magnetotellurik abgeleitet ist: Auslamp -Tisa -Projekt
• Auslamp Delamerian -> Lithosphärischer Widerstandsmodell des delamerischen Orogens aus Auslamp Magnetotelluric -Daten
• Auslamp ne Sa
• Auslamp Gawler
• Auslamp Stationen -> circa 2017
• Tasmanides Papier

Moho

• Moho 2019 und 2023
  • Papier

Mineralablagerungen

• Geologische Umgebung, Alter und Ausstattung der großen australischen Mineralablagerungen
• Ein umfassender Datensatz für die australische Minenproduktion von 1799 bis 2021
  • Papier

Mineralpotential

• Übersicht - Geoscience Australia -> Überblick über Veröffentlichungen und Datensätze
• Sediment veranstaltete Zink
• Bericht
• Sedimente beherbergte Kupfer
• Bericht
• Abstrakt
• Carbonatit -Elemente für Seltene erd
  • Papier

Minenabfall

• Australische Minenabfälle

Einheimischer Titel

• National Native Title Tribunal

Fernerkundung

• Landsat Bare Erde - nackte Erde Median aus Landsat
• Verbesserte barste Erde Landsat -Bilder für Boden- und lithologische Modellierung: Datensatz -> Details einer Verbesserung
• Globaler Bergbau-Fußabdruck aus hochauflösenden Satellitenbildern ** Papier
• Dem -> Australien 1 Sec Srtm DEM verschiedener Sorten

Struktur

• Hauptkrustengrenzen Australiens - 2024 Ausgabe

Geschwindigkeit

• Au Tomo -> Geschwindigkeitsmodell der nächsten Generation der australischen Kruste aus synchroner und asynchroner Umgebungsgeräuschbildgebung

Topographie

• Multiscale topografische Position - RGB
• Info
• Topografischer Nässenindex - 1 und 3 Bogensekunden
• Info
• Topografischer Positionsindex - 1 und 3 Bogensekunden
• Info
• Verwitterungsintensitätsmodell
  • Papier
• Info
• {Info] (https://researchdata.edu.au/wather-intensity-model-australia/1361069)

Nördlich

• Abdeckdicke TISA -> Deckendicke für Tennant Creek MT ISA mit interpolierten Gittern
• Hochauflösende Leitfähigkeitskartierung unter Verwendung regionaler AEM -Umfrage und maschinelles Lernen -> ML Leitfähigkeits -Interpolation für AusaEM
• Erweiterte Zusammenfassung
• Solide Geologie -> Solid Geologie des nordaustralischen Kratons
• Inversionsmodelle -> Die nordaustralischen Craton 3D -Schwerkraft- und Magnetinversionsmodelle
• Ni-Cu-PGE-> Potenzial für intrusion gehostete Ni-Cu-PGE-Sulfidablagerungen in Australien: Eine Analyse des kontinentalen Maßstabs der Prospektivität des Mineralsystems
• TISA IOCG -> Eisenoxid Kupfer -Gold (IOCG) Mineralpotentialbewertung für den Tennant Creek -MT ISA -Region: Geospatial -Daten
• TISA -Veränderung -> Magnetit- und Hämatitveränderungsproxies unter Verwendung von 3D -Schwerkraft und magnetischer Inversion produzieren

Südaustralien

Geologie

• Grundgestein Geologie
• Kristalline Keller -> Kristalline Keller, die sich kreuzen, Bohrlöcher
• Minen und Mineralablagerungen
• Mineralbohrlöcher
• Solid Geology 3d
• 100k Fehler
• Archaer
• Archaische Fehler
• Mesoproterozoic -> Mitte
• Mesoproterozoic -> Mittelfehler
• Mesoproterozoikum -> spät
• Mesoproterozoische Fehler -> Späte Fehler
• Neoproterozoikum
• Neoproterozoische Fehler
• Stuart Regal Sedimentkupfer 3D -Modell
• Oberflächengeologie

Geophysik

• Auslamp 3D -> Magnetotellurische Inversionen
• GCAS -> Gawler Craton Airborne Survey
• Schwerkraft -> Schwerkraftgitter
• Stationen -> Schwerkraftstationen
• Magnetik -> Magnetik
• Seismische Linien -> seismische Linien

Gawler

• Gawler MPP -> Gawler Mineral Promotion Project - Daten

Queensland

• Überblick
• Deep Mining Queensland-Tiefes Bergbau Queensland
• Deposit Atlas -> Northwest Mineral Province Deposit Atlas
• Geologie -> Geologie -Serienübersicht
• Mineral- und Energiebericht -> North -West Queensland Mineral and Energy Province Report 2011 -NWQMEP
• Vektoring -> Mineralgeochemie Vectoring
• Erdölbohrungen
• Kohlenahtgasbrunnen
• Bohrlöcher

Kloncurry

• Toolkit -> Multielement -Toolkit und Labor

Nordgebiet

• Arunta Iocg-> Eisenoxid-Kupfer-Gold-Potenzial der südlichen Arunta-Region
• South Uranium -> südliches Nordgebiet Uran- und Geothermie Energiesystembewertung Digil Datenpaket
• Tennant Creek -> Leitfähigkeitsmodell aus magnetotellurischen Daten in der Ost -Tennant -Region, Northern Territory

New South Wales

Geologie

• Seamless Geology -> NSW Nahtloses Geologie -Datenpaket (ältere Version auch auf dieser Seite)

Mineralpotenzielle Datenpakete

• Curnamona
• Ost Lachlan
• Zentral Lachlan
• Süd -Neuengland

Westaustralien

Geochemie

• Mafic/Ultramaf

Geologie

• 100.000 Grundgestein
• 100K Mapsheets für die Oberfläche Sie müssen einzeln herunterladen und kombinieren - sie sind nicht konsistent
• 250k Mapsheets für die Oberfläche Sie müssen einzeln herunterladen und kombinieren - sie sind nicht konsistent
• 500k Grundgestein
• Verlassene Minen
• Mineralvorkommen

Mineralpotential

• Komatiite verderbte Nickel
• Bericht

Aussicht

• Capricorn-> Prospectivity -Analyse unter Verwendung eines Mineralsystemansatzes - Capricorn -Fallstudienprojekt
• König Leopold -> Mineralische Perspektivität des Königs Leopold Orogen und Lennard Shelf: Analyse potenzieller Felddaten in der Region West Kimberley
• Yilgarn Gold
• Yilgarn 2 -> Vorhersage Mineralentdeckung im östlichen Yilgarn -Kraton: Ein Beispiel für das Targeting eines orogenen Goldmineralsystems im Bezirksmaßstab
• [Shop Note] -> WA hat einige Prospectivity -Pakete zur Verfügung, um auf USB -Laufwerk für 50-60au -Preise zu kaufen -siehe Abschnitt Geospaital Maps

Tasmanien

Geologie

• 250k
• 500K
• 25K
• Mineralvorkommen
• 3D-Modell

Victoria

• Nahtlose Geologie

Neuseeland

• Mineraldatenpaket -> Mineral Exploration Data Pack

Nordamerikanien

• Nationale Geophysikalische, geologische und mineralische Ressourcendaten und Gitter -> hat auch einige Australien -Daten
• Grundwasserbrunnen -> Datenbank
• Maximale horizontale Spannungsorientierung und relative Spannungsgrößen (Fehlregime) Daten in ganz Nordamerika

Kanada

Geologie

• Karte
• Geologie -> Aktualisierte Grundgestein Geologiekarte
• Geologie -> Grundgestein Geologie Zusammenstellung und regionale Synthese von Süd -Rae und Teile von Hearne Domains, Provinz Churchill, Nordwestterritorien, Saskatchewan, Nunavut, Manitoba und Alberta
• Moho -> Nationale Datenbank von Moho -Tiefenschätzungen Schätzungen aus seismischer Brechung und teleseismischen Erhebungen

Geophysik

• DAP -Suche -> Geoportal -Suche - Beachten
• [Schwerkraft, Magnetik, Radiometrie] -> Meistens Landskala

Europa

Finnland

• FODD -> Fennoscandische Mineralablagerungen

Irland

• MPM -> Mineral Potentinal Mapping Project

Papiere mit Code

NLP

• https://www.sciencedirect.com/science/article/pii/s2590197422000064?via%3diHub#bib20- -> Geowissenschaftliche Sprachmodelle und deren intrinsische Bewertung -> NRCAN -Code oben [Inklusive Modell]
• https://www.researchgate.net/publication/334507958_word_embeding_for_application_in_geosciences_development_evaluation_and_examples_of_soil -related_concepts -> Geovec [Beinhaltet das Modell]
• https://www.researchgate.net/publication/347902344_portuguese_word_embeding_for_the_oil_and_gas_industry_development_and_evaluation -> petrovec [integriert das Modell]
• Eine Ressource für die automatisierte Suche und Kollektion geochemischer Datensätze aus Journal -Nahrungsergänzungsmitteln

Geochemie

• 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

Geologie

• https://github.com/sydney-machine-learning/autoencoders_remotesensing-> gestapelte Autocoder für lithologische Zuordnung

Mineral

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

Papiere mit Features -Daten

• Diese können Sie die Ausgabe aus den angegebenen Daten ausgeben.

Mineralische Aussicht

• https://www.sciencedirect.com/science/article/pii/s016913682100010x#s0135 -> Prospectivity -Modellierung von kanadischen magmatischen Ni (± Cu ± Co ± PGE) Sulfid Mineralsysteme [Wurf wertvolle Lesen]
• https://www.sciencedirect.com/science/article/pii/s0169136821006612#b0510 -> Daten -Angesichts der modellierenden Prospectivity -Modellierung von Sediment -Hosted -Zn -PB -Mineralsystemen und ihren kritischen Rohstoffen [gut wert wert.
• https://www.researchgate.net/publication/358956673_towards_a_fully_data-driven_prospectivity_mapping_methodology_a_case_study_of_the_Soutastern_Churchill_province_and_and_labrador_labrador_labrador_labrador

England

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

Geochemie

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

Geologie

• https://eprints.utas.edu.au/32368/ -> maschinen-assistierte Modellierung von Lithologie und Metasomatismus

Geophysik

• https://github.com/tomasnaprstek/aeromagnetic_cnn - aeromagnetische CNN
• Papier https://www.researchgate.net/publication/354772176_convolution_neural_networks_applied_to_the_interpretation_of_lineings_in_aeromagnetetic_data
• PhD -> Neue Methoden zur Interpolation und Interpretation von Linien in aeromagnetischen Daten
• Papier https://www.researchgate.net/publication/354772176_convolution_neural_networks_applied_tthe_interpretation_of_lineAdings_in_aeromagnetic_data -> Konvolution

Geospatial Output - Kein Code

• https://geoscience.data.qld.gov.au/report/cr113697 -> nwmp datengesteuerte Mineralforschung und geologische Zuordnung [CSIRO auch]

Zeitschriften

• https://www.sciencedirect.com/journal/artificial-intelligence-in-geosciences-> künstliche Intelligenz in Geowissenschaften

Papiere

• Im Allgemeinen nicht ML oder kein Code/Daten und manchmal überhaupt keine Verfügbarkeit
• Wird sich schließlich in Dinge unterteilen, die Datenpakete haben oder nicht wie NSW -Zonenstudien.
• Wenn Sie jedoch an einem Bereich interessiert sind, können Sie jedoch häufig ein Bild ein Bild als grobe Führer haben.
• Im Allgemeinen sind diese nicht reproduzierbar - einige wie die Studien zur NSW Prospectivity Zone und NWQMP sind mit einigen Arbeiten.
• Das gelegentliche Papier in diesem Abschnitt kann oben aufgeführt sein

Neu in der Datei

Allgemein

• https://www.researchgate.net/publication/337650865_a_combinative_knowledge-driven_integration_method_for_integrating_geophysical_layers_with_geologische_and_geochemical_datasets
• https://link.springer.com/article/10.1007/s11053-023-10237-W-Eine neue Generation künstlicher Intelligenzalgorithmen für die Mapping Mineral Prospectivity
• https://www.researchgate.net/publication/235443297_addressing_chalengeles_with_exploration_datasets_to_generate_usable_mineral_potential_maps
• https://www.researchgate.net/publication/3300777321_an_impreved_data-driven_multiple_criteria_decision-making_procedure_for_spatial_modeling_of-mineral_prospective_function_function_priction-prodiction-prodiction-
• Künstliche Intelligenz für Mineralforschungen: Eine Überprüfung und Perspektiven zu zukünftigen Richtungen von Data Science -> https://www.sciencedirect.com/science/article/pii/s0012825224002691
• https://www.researchgate.net/project/Bayesian-machine-learning-for-geologic-modeling-and-geophysical-segmentation
• https://www.researchgate.net/publication/229714681_classifiers_for_modeling_of_mineral_potential
• https://www.researchgate.net/publication/352251078_data_analysis_meethods_for_prospective_modelling_as_applied_to_mineral_exploration_targeting_state of the-the-the-the-art_outlook
• https://www.researchgate.net/publication/267927728_data-driven_evidential_belief_modeling_of_mineral_potential_using_few_prospects_and_evidence_with_miss_values
• https://www.linkedin.com/pulse/deep-learning-peets-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_geochemistry_new_insights_from_maachine_algorithm
• https://link.springer.com/article/10.1007/s11430-024-1309-9-> Erklärbare Modelle für künstliche Intelligenz für die Mineral-Prospectivity-Mapping erklärt
• https://www.researchgate.net/publication/229792860_FROM_PREDICTIVE_MAPPING_OF_MINERAL_PROSPORTIVIVITÄT_TO_QUANTITATIVE_STIMATION_OF_NUMBER_OF_UNDISCOVERED_PROSPECTIONS
• https://www.researchgate.net/publication/3399997675_fully_reversible_neural_networks_for_large-scale_surface_and_sub-
  • arxiv
  • Präsentation
  • Konferenz
  • 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_maachine_learning_approach
• https://www.researchgate.net/publication/280013864_Geometric_average_of_spatial_evidence_data_layers_a_gis-basiert_multi-criteria_decision-making_appros_minal_prospivity_maping
• https://www.researchgate.net/publication/355467413_harnessing_the_power_of_artificial_intelligence_and_maachine_learning_in_mineral_exploration-oppportunities_and_calutionary_notes
• https://www.researchgate.net/publication/335819474_importance_of_spatial_prredictor_variable_selection_in_maachine_learning_applications_-moving_from_data_reProduction_to_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/Hood
• https://www.researchgate.net/publication/360660467_lithospheric_conductors_reveal_Source_regions_of_convergent_Mineral_Systems
• https://api.research-repository.uwa.edu.au/portalfiles/portal/5263287/lysytsyn_volodymyr_2015.pdf (PhD-These) GIS-basierte epithmale Kupferprospektivitätskartierung des MT ISA-ISA-ISA-Implikations: Implikationen für Erkundungen für Erkundungen für Erkundungen für Erkundungszielgeräte zu zielen zu zielen
• https://www.researchgate.net/publication/374972769_knowledge_and_technology_transfer_in_and_beyond_mineral_exploration -> Wissens- und Technologieübertragung in und jenseits der Mineralerforschung
• https://www.researchgate.net/publication/331946100_maachine_learning_forn_data-driven_discovery_in_solid_earth_geoscience
• https://thesen.hal.science/tel-04107211/document-Ansätze für maschinelles Lernen für geologische heterogene Quellen unter der Oberfläche
• 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_faults_using_fractal_and_multifractal_models_vectoring_towards_targets
• https://www.researchgate.net/publication/220163838_objective_selection_of_suitable_unit_cell_size_in_data-modeling_of_mineral_prospectivity
• https://www.researchgate.net/publication/273500012_Prediction-area_p-a_plot_and_c-a_fractal_analysis_to_cassify_and_evaluate_evidential_maps_forneral_prospectivity_modeling
• https://www.researchgate.net/publication/354925136_soil-sproble_geochemistry_normalisierte
• https://link.springer.com/article/10.1007/S12665-024-11870-1-> Quantifizierung der Unsicherheit von geowissenschaftlichen Karten, die sich auf das menschliche sensorische Engagement stützen
• https://www.researchgate.net/publication/235443294_the_effect_of_map-scale_on_geological_complexity
• https://www.researchgate.net/publication/235443305_the_effect_f_map_scale_on_geological_complexity_for_computer-aided_exploration_targeting
• https://link.springer.com/article/10.1007/s11053-024-10322-8-> Workflow-induzierte Unsicherheit in der datengesteuerten Mineral-Prospectivity-Mapping

Mineralische Aussicht

Australien

• https://www.mdpi.com/2072-4292/15/16/4074-> Ein räumlicher datengetriebener Ansatz für die Mineral-Prospectivity-Mapping
• 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_metologies_for_the_analysis_of_databases_of_mineral_depossits_and_major_faults
• https://www.researchgate.net/publication/362260616_assessing_the_impact_of_conceptual_Mineral_Systems_uncertainty_on_prospectivity_prredicions
• 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 -> Critical Minerals -Prospectivity -Mapping unter Verwendung von Generativ -AI -AI -AI -A -Kee -A -Keeynote.
• https://www.tandfonline.com/doi/pdf/10.1080/22020586.2019.12073159?Needaccess=true -> Integration eines Mineraliensystems mit maschinellem Lernen: Eine Fallstudie von 'Modern Minerals Exploration' in den MT -Wald in Norden - nach Norden - nach Norden - nach Norden - nach Norden - nach Norden - nach Norden - nach Norden - nach Norden - nach Norden - nach Norden - nach Norden - nach Norden - nach Norden - nach Norden - nach Norden Gawler Craton, Südaustralien
• https://www.researchgate.net/publication/365697240_Mineral_Potential_Modelling_of_orogenic_gold_systems_in_the_granites-tanami_orogen_northern_territory_Auustralia_Multi-Technique_approploytech_aploteconach_aploqueach_aploqueach_aploqueachachachachse_Necthern
• https://publications.csiro.au/publications/publication/picsiro:ep2022-0483 -> Signaturen wichtiger Mineralsysteme in der Provinz Ost -Mount ISA, Queensland: Neue Perspektiven aus Datenanalysen
• https://link.springer.com/article/10.1007/s11004-021-09989-z-> Stochastische Modellierung von Mineral-Explorationszielen
• 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

Argentinien

• 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

Brasilien

• 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

Unscharf

• 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

Zentralafrika

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

Chile

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

China

• https://www.researchgate.net/publication/374968979_3D_cooperative_inversion_of_airborne_magnetic_and_gravity_gradient_data_using_deep_learning_techniques - 3D cooperative inversion of airborne magnetic and gravity gradient data using deep learning techniques [UNSEEN]
• https://www.researchgate.net/publication/369919958_3D_mineral_exploration_Cu-Zn_targeting_with_multi-source_geoscience_datasets_in_the_Weilasituo-bairendaba_district_Inner_Mongolia_China
• https://www.researchgate.net/publication/350817136_3D_Mineral_Prospectivity_Mapping_Based_on_Deep_Metallogenic_Prediction_Theory_A_Case_Study_of_the_Lala_Copper_Mine_Sichuan_China
• https://www.researchgate.net/publication/336771580_3D_Mineral_Prospectivity_Mapping_with_Random_Forests_A_Case_Study_of_Tongling_Anhui_China
• https://www.sciencedirect.com/science/article/pii/S0169136823005772 -> 3D mineral prospectivity modeling in the Sanshandao goldfield, China using the convolutional neural network with attention mechanism
• https://www.sciencedirect.com/science/article/pii/S0009281924001144 -> 3D mineral prospectivity modeling using deep adaptation network transfer learning: A case study of the Xiadian gold deposit, Eastern China
• https://www.sciencedirect.com/science/article/pii/S0009281924000497 -> 3D mineral prospectivity modeling using multi-scale 3D convolution neural network and spatial attention approaches
• https://www.researchgate.net/publication/366201930_3D_Quantitative_Metallogenic_Prediction_of_Indium-Rich_Ore_Bodies_in_the_Dulong_Sn-Zn_Polymetallic_Deposit_Yunnan_Province_SW_China
• https://www.researchgate.net/publication/329600793_A_combined_approach_using_spatially-weighted_principal_components_analysis_and_wavelet_transformation_for_geochemical_anomaly_mapping_in_the_Dashui_ore-concentration_district_Central_China
• https://www.researchgate.net/publication/349034539_A_Comparative_Study_of_Machine_Learning_Models_with_Hyperparameter_Optimization_Algorithm_for_Mapping_Mineral_Prospectivity
• https://www.researchgate.net/publication/354132594_A_Convolutional_Neural_Network_of_GoogLeNet_Applied_in_Mineral_Prospectivity_Prediction_Based_on_Multi-source_Geoinformation
• https://www.researchgate.net/publication/369865076_A_deep-learning-based_mineral_prospectivity_modeling_framework_and_workflow_in_prediction_of_porphyry-epithermal_mineralization_in_the_Duolong_Ore_District_Tibet
• https://www.researchgate.net/publication/374982967_A_Framework_for_Data-Driven_Mineral_Prospectivity_Mapping_with_Interpretable_Machine_Learning_and_Modulated_Predictive_Modeling
• https://www.sciencedirect.com/science/article/pii/S0169136824002026 -> A Global-Local collaborative approach to quantifying spatial non-stationarity in three-dimensional mineral prospectivity modeling
• https://link.springer.com/article/10.1007/s11053-024-10344-2 -> A Heterogeneous Graph Construction Method for Mineral Prospectivity Mapping [UNSEEN]
• https://www.researchgate.net/publication/353421842_A_hybrid_logistic_regression_gene_expression_programming_model_and_its_application_to_mineral_prospectivity_mapping
• https://www.researchgate.net/publication/375764940_A_lightweight_convolutional_neural_network_with_end-to-end_learning_for_three-dimensional_mineral_prospectivity_modeling_A_case_study_of_the_Sanhetun_Area_Heilongjiang_Province_Northeastern_China
• https://www.researchgate.net/publication/339821823_A_Monte_Carlo-based_framework_for_risk-return_analysis_in_mineral_prospectivity_mapping
• https://www.researchgate.net/publication/373715610_A_Multimodal_Learning_Framework_for_Comprehensive_3D_Mineral_Prospectivity_Modeling_with_Jointly_Learned_Structure-Fluid_Relationships
• https://www.sciencedirect.com/science/article/pii/S0169136824001343 -> A novel hybrid ensemble model for mineral prospectivity prediction: A case study in the Malipo W-Sn mineral district, Yunnan Province, China
• https://www.researchgate.net/publication/347344551_A_positive_and_unlabeled_learning_algorithm_for_mineral_prospectivity_mapping
• https://www.researchgate.net/publication/335036019_An_Autoencoder-Based_Dimensionality_Reduction_Algorithm_for_Intelligent_Clustering_of_Mineral_Deposit_Data
• https://www.researchgate.net/publication/363696083_An_Integrated_Framework_for_Data-Driven_Mineral_Prospectivity_Mapping_Using_Bagging-Based_Positive_Unlabeled_Learning_and_Bayesian_Cost-Sensitive_Logistic_Regression
• https://link.springer.com/article/10.1007/s11053-024-10349-x -> An Uncertainty-Quantification Machine Learning Framework for Data-Driven Three-Dimensional Mineral Prospectivity Mapping
• https://link.springer.com/article/10.1007/s11004-023-10076-8 - An Interpretable Graph Attention Network for Mineral Prospectivity Mapping
• https://www.researchgate.net/publication/332751556_Application_of_hierarchical_clustering_singularity_mapping_and_Kohonen_neural_network_to_identify_Ag-Au-Pb-Zn_polymetallic_mineralization_associated_geochemical_anomaly_in_Pangxidong_district
• https://www.mdpi.com/2075-163X/14/9/945 -> Application of Machine Learning to Characterize Metallogenic Potential Based on Trace Elements of Zircon: A Case Study of the Tethyan Domain
• https://www.researchgate.net/publication/339096362_Application_of_nonconventional_mineral_exploration_techniques_case_studies
• https://www.researchgate.net/publication/325702993_Assessment_of_Geochemical_Anomaly_Uncertainty_Through_Geostatistical_Simulation_and_Singularity_Analysis
• https://www.researchgate.net/publication/368586826_Bagging-based_Positive-Unlabeled_Data_Learning_Algorithm_with_Base_Learners_Random_Forest_and_XGBoost_for_3D_Exploration_Targeting_in_the_Kalatongke_District_Xinjiang_China
• https://link.springer.com/article/10.1007/s11004-024-10153-6 -> Causal Discovery and Deep Learning Algorithms for Detecting Geochemical Patterns Associated with Gold-Polymetallic Mineralization: A Case Study of the Edongnan Region [UNSEEN]
• https://www.sciencedirect.com/science/article/pii/S0169136824001409 -> CNN-Transformers for mineral prospectivity mapping in the Maodeng–Baiyinchagan area, Southern Great Xing'an Range
• 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, China
• https://www.researchgate.net/publication/361194407_Visual_Interpretable_Deep_Learning_Algorithm_for_Geochemical_Anomaly_Recognition

Ägypten

• 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

England

• 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

Finnland

• 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

Finnland

• 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

Grönland

• 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

Indien

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

Indonesien

• 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]

Irland

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

Indien

• 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

Norwegen

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

Südkorea

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

Phillipines

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

Russland

• 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

Südafrika

• 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

Spanien

• 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]

Schweden

• 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

Tansania

• 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

Vereinigtes Königreich

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

USA

• 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

Sambia

• 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

Simbabwe

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

GENERAL PAPERS

Übersichten

• 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

Einlagen

• 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

Geochemie

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

Geologie

Änderung

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

Tiefe

• 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

Allgemein

• 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,

Geochronologie

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

Geomorphologie

• 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

Mineralogie

• 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

Stratigraphie

• 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

Tektonik

• 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

Geophysik

Stiftung

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

Allgemein

• 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

Schwerkraft

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

Hyperspectral

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

Joint Inversion

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

Magnetics

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

Magnetotellurics

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

Passive Seismic

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

Seismic

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

Surface Resistivity

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

Unsicherheit

• 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

Karten

• 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

Überblick

• 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

Geochemie

• 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

Unscharf

• 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

Unsicherheit

• 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

Australien

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

South Australia

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

NT

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

WA

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

Brasilien

• 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

Australien

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

SA

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

WA

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

NT

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

NSW

• 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

Brasilien

• 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

Australien

• 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

Tasmanien

• 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

Westaustralien

• 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.

Neuronale Netze

• 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

Allgemein

• 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

Brasilien

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

China

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

Grönland

• 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

Indien

• 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

Spanien

• 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

Andere

• 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

Tiefes Lernen

• 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