[HTML][HTML] Ensemble learning models with a Bayesian optimization algorithm for mineral prospectivity mapping
J Yin, N Li - Ore geology reviews, 2022 - Elsevier
Abstract Machine learning algorithms have been widely applied in mineral prospectivity
mapping (MPM). In this study, we implemented ensemble learning of extreme gradient …
mapping (MPM). In this study, we implemented ensemble learning of extreme gradient …
[HTML][HTML] Mapping prospectivity for regolith-hosted REE deposits via convolutional neural network with generative adversarial network augmented data
The regolith-hosted rare earth elements (REE) deposits are the dominant source of the
global heavy REE resources. This study proposed a convolutional neural network (CNN) …
global heavy REE resources. This study proposed a convolutional neural network (CNN) …
Graph deep learning model for mapping mineral prospectivity
Mineral prospectivity mapping (MPM) aims to reduce the areas for searching of mineral
deposits. Various statistical models that have been successfully adopted to delineate …
deposits. Various statistical models that have been successfully adopted to delineate …
A new generation of artificial intelligence algorithms for mineral prospectivity mapping
Here, we propose a new concept,'new generation artificial intelligence (AI) algorithms for
mineral prospectivity mapping (MPM)', which places greater emphasis on interpretability and …
mineral prospectivity mapping (MPM)', which places greater emphasis on interpretability and …
A geologically constrained variational autoencoder for mineral prospectivity mapping
Deep learning algorithms (DLAs) are becoming popular tools for mineral prospectivity
mapping. However, purely data-driven DLAs frequently ignore expert and domain …
mapping. However, purely data-driven DLAs frequently ignore expert and domain …
Quantification of uncertainty associated with evidence layers in mineral prospectivity mapping using direct sampling and convolutional neural network
Mineral prospectivity mapping (MPM) mainly focuses on searching prospective areas for a
particular type of mineral deposits. However, MPM is typically subject to uncertainties …
particular type of mineral deposits. However, MPM is typically subject to uncertainties …
A physically constrained hybrid deep learning model to mine a geochemical data cube in support of mineral exploration
Geochemical survey data provide rich information on geochemical elemental concentrations
and their spatial patterns in relation to mineralization or pollution. A geochemical data cube …
and their spatial patterns in relation to mineralization or pollution. A geochemical data cube …
Learning 3D mineral prospectivity from 3D geological models using convolutional neural networks: Application to a structure-controlled hydrothermal gold deposit
H Deng, Y Zheng, J Chen, S Yu, K Xiao… - Computers & Geosciences, 2022 - Elsevier
Abstract Three-dimensional (3D) geological models are typical data sources in 3D mineral
prospectivity modeling. However, identifying prospectivity-informative predictor variables …
prospectivity modeling. However, identifying prospectivity-informative predictor variables …
Regularization in machine learning models for MVT Pb-Zn prospectivity mapping: applying lasso and elastic-net algorithms
M Hajihosseinlou, A Maghsoudi… - Earth Science Informatics, 2024 - Springer
The current research employed the least absolute shrinkage and selection operator (Lasso)
and Elastic-net algorithms to examine their potential utilization in MVT Pb-Zn prospectivity …
and Elastic-net algorithms to examine their potential utilization in MVT Pb-Zn prospectivity …
An interpretable graph attention network for mineral prospectivity mapping
Various data-driven mineral prospectivity mapping (MPM) methods have been successfully
adopted to delineate prospecting regions for a specific type of mineral deposit. These …
adopted to delineate prospecting regions for a specific type of mineral deposit. These …