Deep Forest Modeling: An Interpretable Deep Learning Method for Mineral Prospectivity Mapping
Abstract Accurate mineral prediction is crucial for reducing costs and uncertainties in mineral discovery and extraction. The use of artificial intelligence and big data has advanced mineral prediction into intelligent forecasting. Machine learning methods have shown significant promise in enhancing...
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | Journal of Geophysical Research: Machine Learning and Computation |
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| Online Access: | https://doi.org/10.1029/2024JH000311 |
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| author | Yue‐Lin Dong Zhen‐Jie Zhang |
| author_facet | Yue‐Lin Dong Zhen‐Jie Zhang |
| author_sort | Yue‐Lin Dong |
| collection | DOAJ |
| description | Abstract Accurate mineral prediction is crucial for reducing costs and uncertainties in mineral discovery and extraction. The use of artificial intelligence and big data has advanced mineral prediction into intelligent forecasting. Machine learning methods have shown significant promise in enhancing outcomes. Currently, neural network‐based approaches dominate deep learning (DL), but they lack interpretability and have high modeling complexity, making them less effective for complex problems and time‐consuming. Deep Forest, an innovative DL paradigm, addresses these issues by dynamically adjusting complexity and providing importance assessments for predictive factors. This study focuses on the North American Cordillera, known for its rich geological data and potential for porphyry copper deposits (PCDs). Predictions are made using Deep Forest with factors like Euclidean distance between faults and magmatic rock, fault line density, gravity anomalies, and stream‐sediment geochemical data. Deep neural networks, random forest, convolutional neural networks, transformer model and graph convolutional networks are also used for comparison. Deep Forest shows high performance and can avoid the black box problem of DL without relying on other tools in DL, providing a new perspective for the development and application of other non‐neural network DL models for mineral prediction. Feature importance analysis shows that geological structure and magmatism significantly influence PCD prediction. Elevated levels of elements like Al, Co, and Cr in stream sediments help identify mineralization‐related alterations. These findings underscore Deep Forest's capability to accurately and efficiently guide mineral exploration, highlighting its potential as a promising approach for mineral prospecting. |
| format | Article |
| id | doaj-art-c42838b8b10f4530ac1ea5e2d53e3b13 |
| institution | Kabale University |
| issn | 2993-5210 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Geophysical Research: Machine Learning and Computation |
| spelling | doaj-art-c42838b8b10f4530ac1ea5e2d53e3b132025-08-20T03:42:25ZengWileyJournal of Geophysical Research: Machine Learning and Computation2993-52102024-12-0114n/an/a10.1029/2024JH000311Deep Forest Modeling: An Interpretable Deep Learning Method for Mineral Prospectivity MappingYue‐Lin Dong0Zhen‐Jie Zhang1School of Earth Sciences and Resources State Key Lab of Geological Processes and Mineral Resources Frontiers Science Center for Deep‐time Digital Earth China University of Geosciences Beijing ChinaSchool of Earth Sciences and Resources State Key Lab of Geological Processes and Mineral Resources Frontiers Science Center for Deep‐time Digital Earth China University of Geosciences Beijing ChinaAbstract Accurate mineral prediction is crucial for reducing costs and uncertainties in mineral discovery and extraction. The use of artificial intelligence and big data has advanced mineral prediction into intelligent forecasting. Machine learning methods have shown significant promise in enhancing outcomes. Currently, neural network‐based approaches dominate deep learning (DL), but they lack interpretability and have high modeling complexity, making them less effective for complex problems and time‐consuming. Deep Forest, an innovative DL paradigm, addresses these issues by dynamically adjusting complexity and providing importance assessments for predictive factors. This study focuses on the North American Cordillera, known for its rich geological data and potential for porphyry copper deposits (PCDs). Predictions are made using Deep Forest with factors like Euclidean distance between faults and magmatic rock, fault line density, gravity anomalies, and stream‐sediment geochemical data. Deep neural networks, random forest, convolutional neural networks, transformer model and graph convolutional networks are also used for comparison. Deep Forest shows high performance and can avoid the black box problem of DL without relying on other tools in DL, providing a new perspective for the development and application of other non‐neural network DL models for mineral prediction. Feature importance analysis shows that geological structure and magmatism significantly influence PCD prediction. Elevated levels of elements like Al, Co, and Cr in stream sediments help identify mineralization‐related alterations. These findings underscore Deep Forest's capability to accurately and efficiently guide mineral exploration, highlighting its potential as a promising approach for mineral prospecting.https://doi.org/10.1029/2024JH000311Deep ForestCordillera metallogenic beltporphyry coppermineral prospectivity mappingdeep learning |
| spellingShingle | Yue‐Lin Dong Zhen‐Jie Zhang Deep Forest Modeling: An Interpretable Deep Learning Method for Mineral Prospectivity Mapping Journal of Geophysical Research: Machine Learning and Computation Deep Forest Cordillera metallogenic belt porphyry copper mineral prospectivity mapping deep learning |
| title | Deep Forest Modeling: An Interpretable Deep Learning Method for Mineral Prospectivity Mapping |
| title_full | Deep Forest Modeling: An Interpretable Deep Learning Method for Mineral Prospectivity Mapping |
| title_fullStr | Deep Forest Modeling: An Interpretable Deep Learning Method for Mineral Prospectivity Mapping |
| title_full_unstemmed | Deep Forest Modeling: An Interpretable Deep Learning Method for Mineral Prospectivity Mapping |
| title_short | Deep Forest Modeling: An Interpretable Deep Learning Method for Mineral Prospectivity Mapping |
| title_sort | deep forest modeling an interpretable deep learning method for mineral prospectivity mapping |
| topic | Deep Forest Cordillera metallogenic belt porphyry copper mineral prospectivity mapping deep learning |
| url | https://doi.org/10.1029/2024JH000311 |
| work_keys_str_mv | AT yuelindong deepforestmodelinganinterpretabledeeplearningmethodformineralprospectivitymapping AT zhenjiezhang deepforestmodelinganinterpretabledeeplearningmethodformineralprospectivitymapping |