Spatial weighting — An effective incorporation of geological expertise into deep learning models DOI
Wenlei Wang, Chenyi Zhao,

Yixiao Wu

et al.

Geochemistry, Journal Year: 2024, Volume and Issue: unknown, P. 126212 - 126212

Published: Nov. 1, 2024

Language: Английский

Simulation-based mineral prospectivity modeling and Gray Wolf optimization algorithm for delimiting exploration targets DOI Creative Commons
Kamran Mostafaei, Mahyar Yousefi, Oliver P. Kreuzer

et al.

Ore Geology Reviews, Journal Year: 2025, Volume and Issue: unknown, P. 106458 - 106458

Published: Jan. 1, 2025

Language: Английский

Citations

3
Translation of mineral system components into time step-based ore-forming events and evidence maps for mineral exploration: Intelligent mineral prospectivity mapping through adaptation of recurrent neural networks and random forest algorithm DOI Creative Commons
Soran Qaderi, Abbas Maghsoudi, Mahyar Yousefi

et al.

Ore Geology Reviews, Journal Year: 2025, Volume and Issue: unknown, P. 106537 - 106537

Published: Feb. 1, 2025

Language: Английский

Citations

2
Pan-Canadian Predictive Modeling of Lithium–Cesium–Tantalum Pegmatites with Deep Learning and Natural Language Processing DOI Creative Commons
Mohammad Parsa, C J M Lawley, Tarryn Cawood

et al.

Natural Resources Research, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 7, 2025

Abstract The discovery of new lithium resources is essential because plays a vital role in the manufacturing green technology. Along with brines and volcano–sedimentary deposits, approximately one-third share global associated lithium-cesium-tantalum (LCT) pegmatites, Canada hosting numerous examples. This research applied generative adversarial networks, natural language processing, convolutional neural networks to generate mineral prospectivity models support exploration targeting for Canadian LCT pegmatites. Geoscientific text data included within public bedrock geology maps processing were used convert conceptual criteria into evidence layers that complement more traditional, geophysical geochronological modeling (MPM). A multilayer architecture including an attention mechanism, was designed modeling. trained validated using variable synthetically generated class labels, input image sizes, hyperparameters, resulting ensemble 1000 models. uncertainty analyzed risk–return analysis, yielding bivariate choropleth plot facilitates interpretation downstream applications. further complemented by employing post hoc interpretability algorithms translate black-box nature comprehensible content. low-risk high return our reduces search space discovering pegmatites 88%, delineating 99% known Canada. results this study suggest workflow (i.e., combining synthetic generation, propagation MPM) decision-making regional-scale could also be other systems.

Language: Английский

Citations

1
Interpretability Analysis of Data Augmented Convolutional Neural Network in Mineral Prospectivity Mapping Using Black-Box Visualization Tools DOI
Yue Liu, Tao Sun,

Kaixing Wu

et al.

Natural Resources Research, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 31, 2025

Language: Английский

Citations

0
A semi-supervised learning framework for intelligent mineral prospectivity mapping: Incorporation of the CatBoost and Gaussian mixture model algorithms DOI

Mahsa Hajihosseinlou,

Abbas Maghsoudi, Reza Ghezelbash

et al.

Journal of Geochemical Exploration, Journal Year: 2025, Volume and Issue: unknown, P. 107755 - 107755

Published: March 1, 2025

Language: Английский

Citations

0
Predicting Geothermal Heat Flow in the Bohai Bay Basin Based on Machine Learning Methods DOI

Z. Guo,

Kewen Li, Han Zhang

et al.

Mathematical Geosciences, Journal Year: 2025, Volume and Issue: unknown

Published: March 26, 2025

Language: Английский

Citations

0
Mineral Prospectivity Modeling of Graphite Deposits and Occurrences in Canada DOI Creative Commons
Steven E. Zhang, C J M Lawley, Julie E. Bourdeau

et al.

Natural Resources Research, Journal Year: 2025, Volume and Issue: unknown

Published: March 26, 2025

Language: Английский

Citations

0
Geological Knowledge-Embedding Transfer-Learning Architecture for Geochemical Anomaly Identification DOI

Luyi Shi,

Renguang Zuo

Mathematical Geosciences, Journal Year: 2025, Volume and Issue: unknown

Published: April 22, 2025

Language: Английский

Citations

0
Interpretable machine learning for mineral prospectivity mapping in the Qulong–Jiama district, Tibet, China DOI Creative Commons

Nini Mou,

Emmanuel John M. Carranza,

Jianling Xue

et al.

Ore Geology Reviews, Journal Year: 2025, Volume and Issue: 182, P. 106659 - 106659

Published: May 6, 2025

Language: Английский

Citations

0
Advanced deep learning models for predicting elemental concentrations in iron ore mine using XRF data: a cost-effective alternative to ICP-MS methods DOI
Amirhossein Najafabadipour,

Fereshteh Hassanzadeh,

Meghdad Kordestani

et al.

Environmental Geochemistry and Health, Journal Year: 2025, Volume and Issue: 47(4)

Published: March 5, 2025

Language: Английский

Citations

0