On the public citation graph

A Review of Mineral Prospectivity Mapping Using Deep Learning

0 signed · 160 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.

Claim with ORCID

Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.

See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-319-24574-4_28openalex-snapshot

U-Net: Convolutional Networks for Biomedical Image Segmentation

inferredcites →https://doi.org/10.1007/bf02272804openalex-snapshot

Basic concepts in three-part quantitative assessments of undiscovered mineral resources

inferredcites →https://doi.org/10.1007/s00126-018-0800-xopenalex-snapshot

Litho- and chemostratigraphy of the Flatreef PGE deposit, northern Bushveld Complex

inferredcites →https://doi.org/10.1007/s00521-019-04341-3openalex-snapshot

Applications of deep convolutional neural networks in prospecting prediction based on two-dimensional geological big data

inferredcites →https://doi.org/10.1007/s10462-020-09838-1openalex-snapshot

A review on the long short-term memory model

inferredcites →https://doi.org/10.1007/s11004-014-9578-2openalex-snapshot

BoostWofE: A New Sequential Weights of Evidence Model Reducing the Effect of Conditional Dependency

inferredcites →https://doi.org/10.1007/s11004-022-10008-yopenalex-snapshot

Generative Adversarial Network Applied to Ore Type Modeling in Complex Geological Environments

inferredcites →https://doi.org/10.1007/s11053-017-9357-0openalex-snapshot

Big Data Analytics of Identifying Geochemical Anomalies Supported by Machine Learning Methods

inferredcites →https://doi.org/10.1007/s11053-018-9439-7openalex-snapshot

An Improved Prediction-Area Plot for Prospectivity Analysis of Mineral Deposits

inferredcites →https://doi.org/10.1007/s11053-019-09471-yopenalex-snapshot

Mapping Geochemical Anomalies Through Integrating Random Forest and Metric Learning Methods

inferredcites →https://doi.org/10.1007/s11053-019-09483-8openalex-snapshot

Boosting for Mineral Prospectivity Modeling: A New GIS Toolbox

inferredcites →https://doi.org/10.1007/s11053-020-09742-zopenalex-snapshot

Random-Drop Data Augmentation of Deep Convolutional Neural Network for Mineral Prospectivity Mapping

inferredcites →https://doi.org/10.1007/s11053-020-09789-yopenalex-snapshot

Data-driven Mineral Prospectivity Mapping by Joint Application of Unsupervised Convolutional Auto-encoder Network and Supervised Convolutional Neural Network

inferredcites →https://doi.org/10.1007/s11053-021-09934-1openalex-snapshot

A Convolutional Neural Network of GoogLeNet Applied in Mineral Prospectivity Prediction Based on Multi-source Geoinformation

inferredcites →https://doi.org/10.1007/s11053-021-09979-2openalex-snapshot

Mineral Prospectivity Mapping via Gated Recurrent Unit Model

inferredcites →https://doi.org/10.1007/s11053-022-10038-7openalex-snapshot

Mineral Prospectivity Prediction by Integration of Convolutional Autoencoder Network and Random Forest

inferredcites →https://doi.org/10.1007/s11053-023-10272-7openalex-snapshot

A Framework for Data-Driven Mineral Prospectivity Mapping with Interpretable Machine Learning and Modulated Predictive Modeling

inferredcites →https://doi.org/10.1007/s12583-020-1365-zopenalex-snapshot

Mineral Prospectivity Prediction via Convolutional Neural Networks Based on Geological Big Data

inferredcites →https://doi.org/10.1007/s42461-024-01010-5openalex-snapshot

Estimation of Fe Grade at an Ore Deposit Using Extreme Gradient Boosting Trees (XGBoost)

inferredcites →https://doi.org/10.1016/j.apgeochem.2021.104994openalex-snapshot

Detection of the multivariate geochemical anomalies associated with mineralization using a deep convolutional neural network and a pixel-pair feature method

inferredcites →https://doi.org/10.1016/j.apgeochem.2021.105043openalex-snapshot

Detection of geochemical anomalies related to mineralization using the GANomaly network

inferredcites →https://doi.org/10.1016/j.cageo.2004.11.013openalex-snapshot

Multivariate outlier detection in exploration geochemistry

inferredcites →https://doi.org/10.1016/j.cageo.2010.09.014openalex-snapshot

Support vector machine: A tool for mapping mineral prospectivity

inferredcites →https://doi.org/10.1016/j.cageo.2011.11.009openalex-snapshot

Geocomputation of mineral exploration targets

inferredcites →https://doi.org/10.1016/j.cageo.2014.10.004openalex-snapshot

Random forest predictive modeling of mineral prospectivity with small number of prospects and data with missing values in Abra (Philippines)

inferredcites →https://doi.org/10.1016/j.cageo.2015.03.007openalex-snapshot

Prediction–area (P–A) plot and C–A fractal analysis to classify and evaluate evidential maps for mineral prospectivity modeling

inferredcites →https://doi.org/10.1016/j.cageo.2015.10.006openalex-snapshot

Recognition of geochemical anomalies using a deep autoencoder network

inferredcites →https://doi.org/10.1016/j.cageo.2017.10.005openalex-snapshot

GIS-based rare events logistic regression for mineral prospectivity mapping

inferredcites →https://doi.org/10.1016/j.cageo.2019.01.016openalex-snapshot

A Spatially Constrained Multi-Autoencoder approach for multivariate geochemical anomaly recognition

inferredcites →https://doi.org/10.1016/j.cageo.2019.05.011openalex-snapshot

Integration of auto-encoder network with density-based spatial clustering for geochemical anomaly detection for mineral exploration

inferredcites →https://doi.org/10.1016/j.cageo.2021.104974openalex-snapshot

Mineral prospectivity mapping using a joint singularity-based weighting method and long short-term memory network

inferredcites →https://doi.org/10.1016/j.cageo.2022.105075openalex-snapshot

Applications of data augmentation in mineral prospectivity prediction based on convolutional neural networks

inferredcites →https://doi.org/10.1016/j.chemer.2024.126156openalex-snapshot

A SMOTified-GAN-augmented bagging ensemble model of extreme learning machines for detecting geochemical anomalies associated with mineralization

inferredcites →https://doi.org/10.1016/j.compchemeng.2018.04.009openalex-snapshot

Deep convolutional neural network model based chemical process fault diagnosis

inferredcites →https://doi.org/10.1016/j.gexplo.2011.11.001openalex-snapshot

Exploring the effects of cell size in geochemical mapping

inferredcites →https://doi.org/10.1016/j.gexplo.2021.106904openalex-snapshot

Transfer learning and siamese neural network based identification of geochemical anomalies for mineral exploration: A case study from the Cu Au deposit in the NW Junggar area of northern Xinjiang Province, China

inferredcites →https://doi.org/10.1016/j.gexplo.2023.107326openalex-snapshot

Auto encoder generative adversarial networks - based mineral prospectivity mapping in Lhasa area, Tibet

inferredcites →https://doi.org/10.1016/j.gr.2024.09.004openalex-snapshot

Porphyry-type mineral prospectivity mapping with imbalanced data via prior geological transfer learning

inferredcites →https://doi.org/10.1016/j.gsf.2021.101311openalex-snapshot

Integrating deep learning and logging data analytics for lithofacies classification and 3D modeling of tight sandstone reservoirs

inferredcites →https://doi.org/10.1016/j.gsf.2023.101715openalex-snapshot

Leucogranite mapping via convolutional recurrent neural networks and geochemical survey data in the Himalayan orogen

inferredcites →https://doi.org/10.1016/j.jafrearsci.2016.11.032openalex-snapshot

Application of support vector machines for copper potential mapping in Kerman region, Iran

inferredcites →https://doi.org/10.1016/j.jag.2021.102619openalex-snapshot

Lithological mapping enhancement by integrating Sentinel 2 and gamma-ray data utilizing support vector machine: A case study from Egypt

inferredcites →https://doi.org/10.1016/j.jappgeo.2019.103929openalex-snapshot

Research and application of logging lithology identification for igneous reservoirs based on deep learning

inferredcites →https://doi.org/10.1016/j.neunet.2018.07.016openalex-snapshot

Graph structured autoencoder

inferredcites →https://doi.org/10.1016/j.oregeorev.2008.11.004openalex-snapshot

Application of a hybrid method combining multilevel fuzzy comprehensive evaluation with asymmetric fuzzy relation analysis to mapping prospectivity

inferredcites →https://doi.org/10.1016/j.oregeorev.2010.04.002openalex-snapshot

Weights-of-evidence and logistic regression modeling of magmatic nickel sulfide prospectivity in the Yilgarn Craton, Western Australia

inferredcites →https://doi.org/10.1016/j.oregeorev.2012.08.003openalex-snapshot

The mechanics of hydrothermal systems: I. Ore systems as chemical reactors

inferredcites →https://doi.org/10.1016/j.oregeorev.2014.08.010openalex-snapshot

Data-driven predictive mapping of gold prospectivity, Baguio district, Philippines: Application of Random Forests algorithm

inferredcites →https://doi.org/10.1016/j.oregeorev.2014.09.024openalex-snapshot

Evaluation of uncertainty in mineral prospectivity mapping due to missing evidence: A case study with skarn-type Fe deposits in Southwestern Fujian Province, China

inferredcites →https://doi.org/10.1016/j.oregeorev.2015.01.001openalex-snapshot

Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines

inferredcites →https://doi.org/10.1016/j.oregeorev.2015.04.017openalex-snapshot

Introduction to the Special Issue: GIS-based mineral potential modelling and geological data analyses for mineral exploration

inferredcites →https://doi.org/10.1016/j.oregeorev.2015.06.001openalex-snapshot

Three-dimensional mineral prospectivity modeling for targeting of concealed mineralization within the Zhonggu iron orefield, Ningwu Basin, China

inferredcites →https://doi.org/10.1016/j.oregeorev.2015.12.005openalex-snapshot

Mapping mineral prospectivity for Cu polymetallic mineralization in southwest Fujian Province, China

inferredcites →https://doi.org/10.1016/j.oregeorev.2018.10.006openalex-snapshot

Mapping mineral prospectivity through big data analytics and a deep learning algorithm

inferredcites →https://doi.org/10.1016/j.oregeorev.2019.03.016openalex-snapshot

Applying spatial prospectivity mapping to exploration targeting: Fundamental practical issues and suggested solutions for the future

inferredcites →https://doi.org/10.1016/j.oregeorev.2019.04.003openalex-snapshot

GIS-based mineral prospectivity mapping using machine learning methods: A case study from Tongling ore district, eastern China

inferredcites →https://doi.org/10.1016/j.oregeorev.2019.103305openalex-snapshot

Regional metallogeny of Tungsten-tin-polymetallic deposits in Nanling region, South China

inferredcites →https://doi.org/10.1016/j.oregeorev.2020.103677openalex-snapshot

Paragenetic constraints on the Cantung, Mactung and Lened tungsten skarn deposits, Canada: Implications for grade distribution

inferredcites →https://doi.org/10.1016/j.oregeorev.2021.104264openalex-snapshot

Recognition of multivariate geochemical anomalies associated with mineralization using an improved generative adversarial network

inferredcites →https://doi.org/10.1016/j.oregeorev.2021.104316openalex-snapshot

Mineral prospectivity mapping by deep learning method in Yawan-Daqiao area, Gansu

inferredcites →https://doi.org/10.1016/j.oregeorev.2022.104693openalex-snapshot

Mapping prospectivity for regolith-hosted REE deposits via convolutional neural network with generative adversarial network augmented data

inferredcites →https://doi.org/10.1016/j.oregeorev.2022.105277openalex-snapshot

Spatial coupling relationship between faults and gold deposits in the Jiaodong ore concentration area and the effect of thermal doming-extension on mineralisation

inferredcites →https://doi.org/10.1016/j.oregeorev.2023.105381openalex-snapshot

Mineral prospectivity mapping using attention-based convolutional neural network

inferredcites →https://doi.org/10.1016/j.oregeorev.2023.105419openalex-snapshot

A deep-learning-based mineral prospectivity modeling framework and workflow in prediction of porphyry–epithermal mineralization in the Duolong ore District, Tibet

inferredcites →https://doi.org/10.1016/j.oregeorev.2023.105497openalex-snapshot

Evolution of magmatic sulfide of the giant Jinchuan Ni-Cu deposit, NW China: Insights from chalcophile elements in base metal sulfide minerals

inferredcites →https://doi.org/10.1016/j.oregeorev.2023.105665openalex-snapshot

Prospectivity modelling of critical mineral deposits using a generative adversarial network with oversampling and positive-unlabelled bagging

inferredcites →https://doi.org/10.1016/j.oregeorev.2023.105787openalex-snapshot

Mineral prospecting mapping with conditional generative adversarial network augmented data

inferredcites →https://doi.org/10.1016/j.rse.2021.112750openalex-snapshot

A review of machine learning in processing remote sensing data for mineral exploration

inferredcites →https://doi.org/10.1016/j.vlsi.2017.11.002openalex-snapshot

Normalization and dropout for stochastic computing-based deep convolutional neural networks

inferredcites →https://doi.org/10.1016/s2096-5192(22)00087-8openalex-snapshot

Soil geochemical prospecting prediction method based on deep convolutional neural networks-Taking Daqiao Gold Deposit in Gansu Province, China as an example

inferredcites →https://doi.org/10.1023/a:1021193827501openalex-snapshot

Conditional Independence Test for Weights-of-Evidence Modeling

inferredcites →https://doi.org/10.1029/2022jb025933openalex-snapshot

Mineral Texture Classification Using Deep Convolutional Neural Networks: An Application to Zircons From Porphyry Copper Deposits

inferredcites →https://doi.org/10.1038/s41598-023-34531-yopenalex-snapshot

Towards better delineation of hydrothermal alterations via multi-sensor remote sensing and airborne geophysical data

inferredcites →https://doi.org/10.1080/08120099.2017.1328705openalex-snapshot

Mapping mineral prospectivity by using one-class support vector machine to identify multivariate geological anomalies from digital geological survey data

inferredcites →https://doi.org/10.1109/72.279181openalex-snapshot

Learning long-term dependencies with gradient descent is difficult

inferredcites →https://doi.org/10.1109/access.2018.2870203openalex-snapshot

Prospecting Information Extraction by Text Mining Based on Convolutional Neural Networks–A Case Study of the Lala Copper Deposit, China

inferredcites →https://doi.org/10.1109/cvpr.2015.7298594openalex-snapshot

Going deeper with convolutions

inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot

Deep Residual Learning for Image Recognition

inferredcites →https://doi.org/10.1109/jstars.2023.3321714openalex-snapshot

From Pixels to Deposits: Porphyry Mineralization With Multispectral Convolutional Neural Networks

inferredcites →https://doi.org/10.1109/tpwrs.2018.2794541openalex-snapshot

Model-Free Renewable Scenario Generation Using Generative Adversarial Networks

inferredcites →https://doi.org/10.1126/science.1127647openalex-snapshot

Reducing the Dimensionality of Data with Neural Networks

inferredcites →https://doi.org/10.1144/geochem2016-024openalex-snapshot

Application of one-class support vector machine to quickly identify multivariate anomalies from geochemical exploration data

inferredcites →https://doi.org/10.1145/3065386openalex-snapshot

ImageNet classification with deep convolutional neural networks

inferredcites →https://doi.org/10.1162/neco.2006.18.7.1527openalex-snapshot

A Fast Learning Algorithm for Deep Belief Nets

inferredcites →https://doi.org/10.1186/s00015-024-00458-3openalex-snapshot

Direct mineral content prediction from drill core images via transfer learning

inferredcites →https://doi.org/10.1190/int-2014-0190.1openalex-snapshot

Geophysical signature of the Victoria property, vectoring toward deep mineralization in the Sudbury Basin

inferredcites →https://doi.org/10.2113/gsecongeo.74.7.1613openalex-snapshot

Spectra of altered rocks in the visible and near infrared

inferredcites →https://doi.org/10.2113/gsecongeo.85.7.1438openalex-snapshot

Pb isotope bearing on the metallogenesis of sulfide ore deposits in central and southern Peru

inferredcites →https://doi.org/10.3115/v1/w14-4012openalex-snapshot

On the Properties of Neural Machine Translation: Encoder–Decoder Approaches

inferredcites →https://doi.org/10.3389/fncom.2017.00114openalex-snapshot

Editorial: Artificial Neural Networks as Models of Neural Information Processing

inferredcites →https://doi.org/10.3390/app122211433openalex-snapshot

Prediction of Prospecting Target Based on ResNet Convolutional Neural Network

inferredcites →https://doi.org/10.3390/min11060641openalex-snapshot

Multisource Data Analysis for Gold Potentiality Mapping of Atalla Area and Its Environs, Central Eastern Desert, Egypt

inferredcites →https://doi.org/10.3390/min13040504openalex-snapshot

Mineral Prospecting Prediction via Transfer Learning Based on Geological Big Data: A Case Study of Huayuan, Hunan, China

inferredcites →https://doi.org/10.3390/min9050270openalex-snapshot

A Multi-Convolutional Autoencoder Approach to Multivariate Geochemical Anomaly Recognition

inferredcites →https://doi.org/10.3390/rs12081261openalex-snapshot

Integration of Selective Dimensionality Reduction Techniques for Mineral Exploration Using ASTER Satellite Data

inferredcites →https://doi.org/10.3390/rs15020439openalex-snapshot

Mineral Prospectivity Mapping of Porphyry Copper Deposits Based on Remote Sensing Imagery and Geochemical Data in the Duolong Ore District, Tibet

inferredcites →https://doi.org/10.3390/s20236854openalex-snapshot

Hierarchical Spatial-Spectral Feature Extraction with Long Short Term Memory (LSTM) for Mineral Identification Using Hyperspectral Imagery

inferredcites →https://doi.org/10.48550/arxiv.1512.09300openalex-snapshot

Autoencoding beyond pixels using a learned similarity metric

inferredcites →https://doi.org/10.48550/arxiv.1706.08606openalex-snapshot

Cognitive Psychology for Deep Neural Networks: A Shape Bias Case Study

inferredcites →https://doi.org/10.5382/econgeo.4976openalex-snapshot

Timing of Rhyolite Intrusion and Carlin-Type Gold Mineralization at the Cortez Hills Carlin-Type Deposit, Nevada, USA

inferredcites →https://doi.org/10.5382/segnews.2006-66.feaopenalex-snapshot

The Olympic Dam Ore Deposit Discovery — A Personal View

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/978-3-032-11733-5_6openalex-snapshot

Exploring the Impact of Different Loss Functions for Anomaly Prediction in a Mineral Processing Plant

inferred← citeshttps://doi.org/10.1007/s10064-026-04816-3openalex-snapshot

Characterization of Fractures and Veins from Images of Drill Core: A review of imaging technologies

inferred← citeshttps://doi.org/10.1007/s10596-026-10487-6openalex-snapshot

Sandstone-hosted uranium mineral prospectivity mapping based on ResNet50 self-distillation

inferred← citeshttps://doi.org/10.1007/s11004-025-10220-6openalex-snapshot

Advancing Data-Driven Mineral Prospectivity Mapping: Benchmarking Deep Forest Against Deep Neural Network Models

inferred← citeshttps://doi.org/10.1007/s11004-026-10318-5openalex-snapshot

GeoKIM: Label-Informed Masked Representation Learning for Geoscientific Data Under Few-Shot Conditions

inferred← citeshttps://doi.org/10.1007/s11004-026-10351-4openalex-snapshot

MPM-KAN: An Interpretable Data-Driven Framework for Mineral Prospectivity Mapping Based on Kolmogorov–Arnold Networks

inferred← citeshttps://doi.org/10.1007/s11053-025-10560-4openalex-snapshot

Bibliometric Analysis of Mineral Prospectivity Mapping Research from 2006 to 2024

inferred← citeshttps://doi.org/10.1007/s11053-026-10712-0openalex-snapshot

Deep Learning and Explainable AI for Critical Mineral Prospectivity Mapping: Multi-target Prediction with Uncertainty Quantification

inferred← citeshttps://doi.org/10.1007/s11053-026-10763-3openalex-snapshot

A Multi-Level Stacking Ensemble Architecture: Advantages of Random Forest, Extreme Gradient Boosting, and Light Gradient Boosting Machine for Porphyry-Related Al-Based Mineral Prospectivity Mapping

inferred← citeshttps://doi.org/10.1007/s11053-026-10778-wopenalex-snapshot

Uncertainty-Aware Mineral Prospectivity Mapping with Positive-Unlabeled Learning and Conformal Calibration: Ni Mineralization in the Yilgarn Craton

inferred← citeshttps://doi.org/10.1007/s11053-026-10780-2openalex-snapshot

A Hybrid 3D CNN-GAT Framework with Entropy-Guided Adaptive Fusion for 3D Mineral Prospectivity Modeling

inferred← citeshttps://doi.org/10.1007/s12145-026-02206-7openalex-snapshot

Data-driven tectonic prospectivity modeling for granite-hosted uranium systems: integrating empirical scaling and spatial statistics

inferred← citeshttps://doi.org/10.1007/s12145-026-02252-1openalex-snapshot

Mineral prospectivity mapping driven by multi-source data fusion: application of convolutional neural networks in the Jinchang gold deposit, NE China

inferred← citeshttps://doi.org/10.1007/s12524-025-02314-9openalex-snapshot

Unsupervised AI-Driven MPM: Application of K-Means, K-Medoids and Self-Organizing Maps

inferred← citeshttps://doi.org/10.1007/s13563-026-00656-5openalex-snapshot

Critical minerals in 2025: a global overview with preliminary insights from India

inferred← citeshttps://doi.org/10.1007/s42990-026-00220-xopenalex-snapshot

Machine and deep learning in geological applications: a review of advances, challenges, and future research directions

inferred← citeshttps://doi.org/10.1016/j.acags.2026.100372openalex-snapshot

Integration of drill-core hyperspectral and geochemical data by deep learning to enhance drill-core mineral mapping

inferred← citeshttps://doi.org/10.1016/j.aiig.2026.100285openalex-snapshot

Autoencoder-assisted variational Gaussian process mineral prospectivity mapping for the Okiep Copper District, South Africa

inferred← citeshttps://doi.org/10.1016/j.eswa.2025.128286openalex-snapshot

Mineral resource prediction based on representation learning

inferred← citeshttps://doi.org/10.1016/j.geogeo.2025.100473openalex-snapshot

Advanced machine learning based gold prospectivity mapping in the Dharwar Craton, India: A hybrid knowledge-data driven paradigm integrating ensemble and deep learning

inferred← citeshttps://doi.org/10.1016/j.gexplo.2026.108074openalex-snapshot

A new workflow for uncertainty analysis in AI-driven MPM: Low-risk targeting by DBSCAN algorithm

inferred← citeshttps://doi.org/10.1016/j.gsf.2026.102458openalex-snapshot

Geoscience of the energy–climate–sustainability nexus: Earth system processes, resources, and pathways to a sustainable future

inferred← citeshttps://doi.org/10.1016/j.jafrearsci.2025.105791openalex-snapshot

Predictive modeling of orogenic gold prospectivity in the Wawa area, western Nigeria, using three most commonly-used machine learning algorithms

inferred← citeshttps://doi.org/10.1016/j.jafrearsci.2026.106332openalex-snapshot

Coupled spectral, geophysical, and topographic analysis for targeting tin-dominated polymetallic mineralization in the Kaiama–Shirigberia Basement Complex, Nigeria

inferred← citeshttps://doi.org/10.1016/j.jag.2026.105254openalex-snapshot

A systematic review of deep learning methods for mineral exploration using multisource geoscience data (2018–2025)

inferred← citeshttps://doi.org/10.1016/j.jappgeo.2026.106183openalex-snapshot

Hybrid GIS-machine learning approach for base metal prospectivity mapping in the Gawler Craton, South Australia

inferred← citeshttps://doi.org/10.1016/j.jseaes.2025.106551openalex-snapshot

Regional-scale exploration targeting of REEs associated with Kiruna-type Iron Oxide-Apatite mineralization in the Bafq-Saghand metallogenic province, Central Iran

inferred← citeshttps://doi.org/10.1016/j.oregeorev.2025.106768openalex-snapshot

Multi-model decision system: An ensemble deep learning model to enhance predictive power in mineral prospectivity mapping

inferred← citeshttps://doi.org/10.1016/j.oregeorev.2026.107194openalex-snapshot

Predictive modeling of mineral prospectivity by deep self-organizing map: Implications for copper exploration targeting in saveh district, central Iran

inferred← citeshttps://doi.org/10.1016/j.oregeorev.2026.107250openalex-snapshot

Deep autoencoder-guided machine learning for prospectivity mapping of intrusion-related Cu-Au mineral systems in southwestern New Brunswick, Canada

inferred← citeshttps://doi.org/10.1016/j.oregeorev.2026.107310openalex-snapshot

Comparative evaluation of random forest and graph neural networks for gold prospectivity in the Hatu–Sartohay Belt, China

inferred← citeshttps://doi.org/10.1016/j.oregeorev.2026.107364openalex-snapshot

Dual-relation heterogeneous graph approach with positive-unlabeled learning for mineral prospectivity mapping

inferred← citeshttps://doi.org/10.1016/j.oregeorev.2026.107425openalex-snapshot

A hybrid gold prospectivity mapping model integrating computer vision and token selective attention: a case study of the As-Safra region

inferred← citeshttps://doi.org/10.1016/j.oregeorev.2026.107428openalex-snapshot

Suitability of available regional geoscientific data for mapping areas with concealed orogenic gold mineralization using machine and deep learning algorithms in the Amalia–Kraaipan greenstone Belt, South Africa

inferred← citeshttps://doi.org/10.1016/j.oregeorev.2026.107584openalex-snapshot

Accounting for metallogenic heterogeneity in ensemble learning-based mineral prospectivity mapping: A case study of copper mineralization in eastern Junggar, Xinjiang, China

inferred← citeshttps://doi.org/10.1016/j.oregeorev.2026.107590openalex-snapshot

A Spectral–Semantic integration framework for hydrothermal alteration characterization using Landsat-8 OLI and AlphaEarth Foundations in the Eastern Tien Shan, China

inferred← citeshttps://doi.org/10.1016/j.rsase.2026.101980openalex-snapshot

A metaheuristic design for hyperparameter tuning of XGBoost: Towards shaping an impeccable strategy for predictive modeling of mineral prospectivity

inferred← citeshttps://doi.org/10.1080/10106049.2026.2677268openalex-snapshot

Enhancing mineral exploration in the Kolwezi region, DRC, through remote sensing and ensemble machine learning algorithms

inferred← citeshttps://doi.org/10.1177/25726838261480428openalex-snapshot

Analysis of anomaly characteristics in mineralized segments using core drilling and geophysical logging

inferred← citeshttps://doi.org/10.21285/2686-9993-2025-48-3-310-320openalex-snapshot

Integration of artificial intelligence into geological and geophysical data processing in solid minerals exploration

inferred← citeshttps://doi.org/10.32390/ksmer.2026.63.3.348openalex-snapshot

Machine and Deep Learning Techniques for Critical Mineral Exploration: A Review of International Research Trends and Applications by Deposit Type

inferred← citeshttps://doi.org/10.3389/feart.2025.1743903openalex-snapshot

Future technology strategies for the geoscience sector: insights from STEEP and SWOT analyses

inferred← citeshttps://doi.org/10.3389/feart.2026.1842521openalex-snapshot

Explainable convolutional neural network for iron ore prospectivity mapping: a case study of the Yemaquan district, Qinghai, China

inferred← citeshttps://doi.org/10.3390/asi9060114openalex-snapshot

Advanced Numerical Methods for a First-Kind Fredholm Integral Equation in Potential Field Continuation

inferred← citeshttps://doi.org/10.3390/geosciences16060244openalex-snapshot

Geology-Guided Fixed-Group Fusion ResUNet for Predicting Calcrete-Type Uranium Prospectivity: A Case Study from the Yilgarn Craton, Western Australia

inferred← citeshttps://doi.org/10.3390/min15111119openalex-snapshot

Identifying High-Potential Zones for Iron Mineralization in Bahia, Brazil, Using a Spectral Angle Mapper–Random Forest Integrated Framework

inferred← citeshttps://doi.org/10.3390/min15111128openalex-snapshot

Geostatistically Enhanced Learning for Supervised Classification of Wall-Rock Alteration Using Assay Grades of Trace Elements and Sulfides

inferred← citeshttps://doi.org/10.3390/min16050450openalex-snapshot

Mineral Prospectivity Mapping Based on a Lightweight Two-Dimensional Fully Convolutional Neural Network: A Case Study of the Gold Deposits in the Xiong’ershan Area, Henan Province, China

inferred← citeshttps://doi.org/10.3390/min16060584openalex-snapshot

Recent Advances and Future Perspectives of AI-Based Mineral Exploration: A Review of Machine Learning, Deep Learning, and Geologically Informed Approaches

inferred← citeshttps://doi.org/10.3390/min16060628openalex-snapshot

Comparative Evaluation of Unsupervised Machine Learning Methods for Orogenic Gold Exploration Using Stream Sediment Geochemistry

inferred← citeshttps://doi.org/10.3390/min16080783openalex-snapshot

A Novel Spatial–Element Transformer with Dual Attention Architecture for Mineral Prospectivity Mapping

inferred← citeshttps://doi.org/10.3390/min16101003openalex-snapshot

Explainable Machine Learning in Mineral Prospectivity Mapping: A Critical Review of Methods, Geological Knowledge Embedding, Validation, and Future Directions

inferred← citeshttps://doi.org/10.3390/mining6040091openalex-snapshot

Hyperparameter Optimization of Convolutional Neural Networks Using the Grey Wolf Optimizer for Mineral Prospectivity Mapping

inferred← citeshttps://doi.org/10.3390/sym18060930openalex-snapshot

Predictive Modeling of Lithium Mineralization Using Geospatial Data and Machine Learning Methods in the Kalba–Narym Metallogenic Zone

inferred← citeshttps://doi.org/10.3724/j.issn.1007-2802.20260065openalex-snapshot

Intelligent mineral prospectivity mapping based on the multi-view graph learning

inferred← citeshttps://doi.org/10.3799/dqkx.2025.198openalex-snapshot

基于因果推理模型和图注意力网络的安庆地区矽卡岩型铜矿床三维成矿预测方法

inferred← citeshttps://doi.org/10.5194/gmd-19-2593-2026openalex-snapshot

DEEP-SEAM: an explainable semi-supervised deep learning framework for mineral prospectivity mapping

inferred← citeshttps://doi.org/10.52209/1609-1825_2025_4_78openalex-snapshot

Development of a Digital Geological Database for the Kalba-Narym Ore Belt, East Kazakhstan

inferred← citeshttps://doi.org/10.5564/mgs.v31i62.4567openalex-snapshot

Integrated Random Forest-based porphyry copper prospectivity mapping with EnMAP hyperspectral validation in southeastern Mongolia

Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.