Machine Learning Reveals Magmatic Fertility of Skarn-Type Tungsten Deposits
The chemical composition of apatite has been utilized as an indicator of magmatic fertility related to tungsten mineralization in skarn systems. In this study, we compiled 5776 apatite trace element data from 374 intrusions, along with records indicating magmatic fertility. Then we trained and valid...
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2025-05-01
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| author | Rui-Chang Tan Yong-Jun Shao Yi-Qu Xiong Zhi-Wei Fan Hong-Fei Di Zhao-Jun Wang Kang-Qi Xu |
| author_facet | Rui-Chang Tan Yong-Jun Shao Yi-Qu Xiong Zhi-Wei Fan Hong-Fei Di Zhao-Jun Wang Kang-Qi Xu |
| author_sort | Rui-Chang Tan |
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| description | The chemical composition of apatite has been utilized as an indicator of magmatic fertility related to tungsten mineralization in skarn systems. In this study, we compiled 5776 apatite trace element data from 374 intrusions, along with records indicating magmatic fertility. Then we trained and validated machine learning (ML) models, specifically support vector machine (SVM) and random forests (RF), to classify magmatic fertility based on apatite chemistry in igneous rocks. RF model achieved high classification accuracies (~93%) on the test dataset, demonstrating that employing ML approaches to distinguish apatite derived from fertile versus barren magmas is feasible and effective. Furthermore, we optimized classification thresholds to maximize the model’s predictive accuracy for identifying potentially fertile magmas. Feature-importance analysis of the machine learning classifier shows that elevated La, Yb, and Mn, together with depleted Sr, Y, Gd, and Tb, constitute the most diagnostic elemental signatures of magmatic fertility. As a case study, we applied our trained ML model to predict the magmatic fertility of apatite samples from the Nanling Range (southern China’s largest skarn-type tungsten mineralization province). Benefiting from the application of GAN-based techniques to address sample imbalance, our ML models can effectively identify tungsten-mineralized favorable skarn areas. Additionally, the visualization technique t-distributed stochastic neighbor embedding (t-SNE) was employed to validate and assess classification outcomes. Results showed clear separation between fertile and barren categories within the reduced 3D space. Our findings emphasize apatite as a sensitive indicator mineral for granite-related magmatic fertility and metallogenesis, underscoring its significant potential in mineral exploration. Finally, we provide a convenient prediction software for magmatic fertility based on a machine learning model utilizing apatite trace element compositions. |
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| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-0eba2ed8c37f4d8a8a1af5fbfbaa9c802025-08-20T03:47:49ZengMDPI AGApplied Sciences2076-34172025-05-011510523710.3390/app15105237Machine Learning Reveals Magmatic Fertility of Skarn-Type Tungsten DepositsRui-Chang Tan0Yong-Jun Shao1Yi-Qu Xiong2Zhi-Wei Fan3Hong-Fei Di4Zhao-Jun Wang5Kang-Qi Xu6Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, Changsha 410083, ChinaKey Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, Changsha 410083, ChinaKey Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, Changsha 410083, ChinaKey Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, Changsha 410083, ChinaKey Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, Changsha 410083, ChinaKey Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, Changsha 410083, ChinaKey Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, Changsha 410083, ChinaThe chemical composition of apatite has been utilized as an indicator of magmatic fertility related to tungsten mineralization in skarn systems. In this study, we compiled 5776 apatite trace element data from 374 intrusions, along with records indicating magmatic fertility. Then we trained and validated machine learning (ML) models, specifically support vector machine (SVM) and random forests (RF), to classify magmatic fertility based on apatite chemistry in igneous rocks. RF model achieved high classification accuracies (~93%) on the test dataset, demonstrating that employing ML approaches to distinguish apatite derived from fertile versus barren magmas is feasible and effective. Furthermore, we optimized classification thresholds to maximize the model’s predictive accuracy for identifying potentially fertile magmas. Feature-importance analysis of the machine learning classifier shows that elevated La, Yb, and Mn, together with depleted Sr, Y, Gd, and Tb, constitute the most diagnostic elemental signatures of magmatic fertility. As a case study, we applied our trained ML model to predict the magmatic fertility of apatite samples from the Nanling Range (southern China’s largest skarn-type tungsten mineralization province). Benefiting from the application of GAN-based techniques to address sample imbalance, our ML models can effectively identify tungsten-mineralized favorable skarn areas. Additionally, the visualization technique t-distributed stochastic neighbor embedding (t-SNE) was employed to validate and assess classification outcomes. Results showed clear separation between fertile and barren categories within the reduced 3D space. Our findings emphasize apatite as a sensitive indicator mineral for granite-related magmatic fertility and metallogenesis, underscoring its significant potential in mineral exploration. Finally, we provide a convenient prediction software for magmatic fertility based on a machine learning model utilizing apatite trace element compositions.https://www.mdpi.com/2076-3417/15/10/5237apatitetrace elementsmachine learningmagmatic fertilityskarn-type tungsten deposits |
| spellingShingle | Rui-Chang Tan Yong-Jun Shao Yi-Qu Xiong Zhi-Wei Fan Hong-Fei Di Zhao-Jun Wang Kang-Qi Xu Machine Learning Reveals Magmatic Fertility of Skarn-Type Tungsten Deposits Applied Sciences apatite trace elements machine learning magmatic fertility skarn-type tungsten deposits |
| title | Machine Learning Reveals Magmatic Fertility of Skarn-Type Tungsten Deposits |
| title_full | Machine Learning Reveals Magmatic Fertility of Skarn-Type Tungsten Deposits |
| title_fullStr | Machine Learning Reveals Magmatic Fertility of Skarn-Type Tungsten Deposits |
| title_full_unstemmed | Machine Learning Reveals Magmatic Fertility of Skarn-Type Tungsten Deposits |
| title_short | Machine Learning Reveals Magmatic Fertility of Skarn-Type Tungsten Deposits |
| title_sort | machine learning reveals magmatic fertility of skarn type tungsten deposits |
| topic | apatite trace elements machine learning magmatic fertility skarn-type tungsten deposits |
| url | https://www.mdpi.com/2076-3417/15/10/5237 |
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