GeoHybridGNN: A Hybrid Intelligent Mapping Framework for Porphyry Copper Prospectivity Mapping Integrating Remote Sensing, Geology, and Geochemistry
5 affiliations
- 1College of Geoexploration Science & Technology, Jilin University, Changchun 130061, China
- 2Institute of Integrated Information for Mineral Resources Prediction, Jilin University, Changchun 130061, China
- 3Xinjiang Academy of Geological Research, Urumqi 830057, China
- 4School of Mathematics, Jilin University, Changchun 130061, China
- 5Department of Software Engineering, Government University, Faisalabad 38000, Pakistan
Abstract
The Western Chagai Belt of Pakistan hosts major porphyry Cu-Au systems, but prospectivity mapping in this arc remains difficult because favorable lithology, intrusive bodies, fault corridors, hydrothermal alteration, and Cu geochemical anomalies are spatially heterogeneous across a structurally complex and arid terrain. These conditions create a scientific need for an integrated mapping framework that can combine remote sensing alteration evidence, geology, structure, and geochemistry within a unified and reproducible workflow. This study presents GeoHybridGNN, a hybrid deep learning framework for porphyry copper prospectivity mapping in the Western Chagai Belt. The framework integrates multi-source raster evidence, including remote sensing-derived spectral alteration indices, a Cu geochemical raster, and distance-to-fault information, with graph-based node representations that combine regular neighborhood adjacency on retained grid cells with node attributes derived from lithology and aligned geoscientific raster summaries. All predictors were harmonized to a common 30 m reference raster grid and evaluated using five-fold spatial block cross-validation to provide a more spatially realistic assessment than ordinary random splitting. The implemented model combines a CNN-based raster patch encoder with a GraphSAGE-based graph classifier. Raster patches extracted around graph nodes are encoded into 64-dimensional embeddings, and these embeddings are concatenated with node-level graph features before full-batch graph learning and prediction. Copper occurrences were used only for supervised label assignment and evaluation and were not used as predictive inputs. The results show that GeoHybridGNN produces spatially coherent prospectivity maps, stable fold-wise prediction patterns, and improved target delineation relative to the tested comparison models. Cu geochemical integration produces only a limited change in global discrimination but provides modest local target sharpening in selected zones. These results indicate that GeoHybridGNN can serve as an uncertainty-aware and geologically constrained decision support workflow for porphyry copper targeting. More broadly, the framework provides a transparent strategy for exploration screening in structurally complex and data-heterogeneous metallogenic belts where remote sensing, geological, structural, and geochemical evidence must be integrated consistently.
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BibTeX
@article{bilal2026geohybridgnn,
title = {{GeoHybridGNN: A Hybrid Intelligent Mapping Framework for Porphyry Copper Prospectivity Mapping Integrating Remote Sensing, Geology, and Geochemistry}},
author = {Muhammad Atif Bilal and Yongzhi Wang and Kateryna Hlyniana and Zubair Nabi},
journal = {Remote Sensing},
year = {2026},
month = may,
doi = {10.3390/rs18101638},
url = {https://doi.org/10.3390/rs18101638},
note = {CC BY},
}
RIS
TY - JOUR
TI - GeoHybridGNN: A Hybrid Intelligent Mapping Framework for Porphyry Copper Prospectivity Mapping Integrating Remote Sensing, Geology, and Geochemistry
AU - Muhammad Atif Bilal
AU - Yongzhi Wang
AU - Kateryna Hlyniana
AU - Zubair Nabi
PY - 2026
DA - 2026/05/19
JO - Remote Sensing
DO - 10.3390/rs18101638
UR - https://doi.org/10.3390/rs18101638
AB - The Western Chagai Belt of Pakistan hosts major porphyry Cu-Au systems, but prospectivity mapping in this arc remains difficult because favorable lithology, intrusive bodies, fault corridors, hydrothermal alteration, and Cu geochemical anomalies are spatially heterogeneous across a structurally complex and arid terrain. These conditions create a scientific need for an integrated mapping framework that can combine remote sensing alteration evidence, geology, structure, and geochemistry within a unified and reproducible workflow. This study presents GeoHybridGNN, a hybrid deep learning framework for porphyry copper prospectivity mapping in the Western Chagai Belt. The framework integrates multi-source raster evidence, including remote sensing-derived spectral alteration indices, a Cu geochemical raster, and distance-to-fault information, with graph-based node representations that combine regular neighborhood adjacency on retained grid cells with node attributes derived from lithology and aligned geoscientific raster summaries. All predictors were harmonized to a common 30 m reference raster grid and evaluated using five-fold spatial block cross-validation to provide a more spatially realistic assessment than ordinary random splitting. The implemented model combines a CNN-based raster patch encoder with a GraphSAGE-based graph classifier. Raster patches extracted around graph nodes are encoded into 64-dimensional embeddings, and these embeddings are concatenated with node-level graph features before full-batch graph learning and prediction. Copper occurrences were used only for supervised label assignment and evaluation and were not used as predictive inputs. The results show that GeoHybridGNN produces spatially coherent prospectivity maps, stable fold-wise prediction patterns, and improved target delineation relative to the tested comparison models. Cu geochemical integration produces only a limited change in global discrimination but provides modest local target sharpening in selected zones. These results indicate that GeoHybridGNN can serve as an uncertainty-aware and geologically constrained decision support workflow for porphyry copper targeting. More broadly, the framework provides a transparent strategy for exploration screening in structurally complex and data-heterogeneous metallogenic belts where remote sensing, geological, structural, and geochemical evidence must be integrated consistently.
SN - 2072-4292
ER -