Advances in intelligent extraction and identification of metallogenic information for mineral prospectivity mapping
Abstract
Objective The exploration of deep, concealed, and covered mineral resources is increasingly affected by scale mismatches, background interference, and ambiguous geoscientific responses. This study aims to clarify the hierarchical relationships and methodological pathways through which metallogenic knowledge can be transformed into computable and verifiable predictive information. Methods Structural geological, geochemical, geophysical remote-sensing, and three-dimensional spatial information were systematically reviewed. The review covered spatial quantification, statistical analysis, conventional machine learning, deep learning, geological-knowledge constraints, and spatial validation. A hierarchical framework of "raw data–geological objects and anomalous responses–metallogenic information–spatial representation–predictive variables–quantitative prediction–validation" was established, and different methods were compared in terms of data organization, applicable scale, sample design, interpretability, and validation strategy. Results Six classes of mineral system information were synthesized, including material sources, migration pathways, favorable sites for mineralization, precipitation and enrichment, preservation conditions, and known mineralization. Two principal spatial representations were summarized, namely two-dimensional thematic maps/evidence layers and three-dimensional attribute volumes. Six technical components were identified: information spatialization, anomaly and multivariate-association extraction, conventional machine-learning recognition, deep spatial representation, geological knowledge constraints, and spatial or engineering validation. Both convolutional neural networks (CNNs) and graph neural networks/graph convolutional networks (GNNs/GCNs) belong to the field of deep learning, but are suited to different data structures: CNNs are generally better suited to regular grids, including two-dimensional rasters and three-dimensional voxels, whereas GNNs/GCNs are more suitable for irregular graph-structured data. Random sample splitting may cause spatial leakage, while spatial-block, leave-area-out, cross-district, and engineering validation are more appropriate for evaluating model transferability. Conclusions Metallogenic information is computable evidence produced by quantifying metallogenic knowledge. Its reliability depends primarily on geological semantics, scale compatibility, data quality, sample representativeness, and independent validation rather than algorithmic complexity. Intelligent extraction and identification of metallogenic information should therefore remain constrained by mineral-system concepts, deposit models, and prospecting models, with future emphasis on multisource integration, coordinated two- and three-dimensional representation, uncertainty characterization, and interpretable learning.
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BibTeX
@article{keyan2026advances,
title = {{Advances in intelligent extraction and identification of metallogenic information for mineral prospectivity mapping}},
author = {XIAO Keyan and WANG Zhengyao and LIU Bingli and LI Cheng and CAO Changjie and KONG Yunhui and WANG Yao},
journal = {Dizhi lixue xuebao},
year = {2026},
month = sep,
doi = {10.12090/j.issn.1006-6616.2026098},
url = {https://doi.org/10.12090/j.issn.1006-6616.2026098},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Advances in intelligent extraction and identification of metallogenic information for mineral prospectivity mapping
AU - XIAO Keyan
AU - WANG Zhengyao
AU - LIU Bingli
AU - LI Cheng
AU - CAO Changjie
AU - KONG Yunhui
AU - WANG Yao
PY - 2026
DA - 2026/09/01
JO - Dizhi lixue xuebao
DO - 10.12090/j.issn.1006-6616.2026098
UR - https://doi.org/10.12090/j.issn.1006-6616.2026098
AB - Objective The exploration of deep, concealed, and covered mineral resources is increasingly affected by scale mismatches, background interference, and ambiguous geoscientific responses. This study aims to clarify the hierarchical relationships and methodological pathways through which metallogenic knowledge can be transformed into computable and verifiable predictive information. Methods Structural geological, geochemical, geophysical remote-sensing, and three-dimensional spatial information were systematically reviewed. The review covered spatial quantification, statistical analysis, conventional machine learning, deep learning, geological-knowledge constraints, and spatial validation. A hierarchical framework of "raw data–geological objects and anomalous responses–metallogenic information–spatial representation–predictive variables–quantitative prediction–validation" was established, and different methods were compared in terms of data organization, applicable scale, sample design, interpretability, and validation strategy. Results Six classes of mineral system information were synthesized, including material sources, migration pathways, favorable sites for mineralization, precipitation and enrichment, preservation conditions, and known mineralization. Two principal spatial representations were summarized, namely two-dimensional thematic maps/evidence layers and three-dimensional attribute volumes. Six technical components were identified: information spatialization, anomaly and multivariate-association extraction, conventional machine-learning recognition, deep spatial representation, geological knowledge constraints, and spatial or engineering validation. Both convolutional neural networks (CNNs) and graph neural networks/graph convolutional networks (GNNs/GCNs) belong to the field of deep learning, but are suited to different data structures: CNNs are generally better suited to regular grids, including two-dimensional rasters and three-dimensional voxels, whereas GNNs/GCNs are more suitable for irregular graph-structured data. Random sample splitting may cause spatial leakage, while spatial-block, leave-area-out, cross-district, and engineering validation are more appropriate for evaluating model transferability. Conclusions Metallogenic information is computable evidence produced by quantifying metallogenic knowledge. Its reliability depends primarily on geological semantics, scale compatibility, data quality, sample representativeness, and independent validation rather than algorithmic complexity. Intelligent extraction and identification of metallogenic information should therefore remain constrained by mineral-system concepts, deposit models, and prospecting models, with future emphasis on multisource integration, coordinated two- and three-dimensional representation, uncertainty characterization, and interpretable learning.
SN - 1006-6616
ER -