Xiancheng Mao, Jiaxuan Song, Zhankun Liu, Hao Deng, Jin Chen, Shuyan Yu, Yanan Wang, Ruike Xu, Yuanqian Nie, Yang Zheng
{"title":"基于特征自适应融合策略的阿西浅成低温热液金矿三维找矿前景建模","authors":"Xiancheng Mao, Jiaxuan Song, Zhankun Liu, Hao Deng, Jin Chen, Shuyan Yu, Yanan Wang, Ruike Xu, Yuanqian Nie, Yang Zheng","doi":"10.1016/j.chemer.2024.126190","DOIUrl":null,"url":null,"abstract":"<div><div>Mineralization distribution is always intricately affected by multiple ore-controlling geological units that play different roles in a mineral system (e.g., driver, trap, and throttle). How to effectively balance and integrate ore-controlling features from various 3D geological models during 3D mineral prospectivity modeling (MPM) is still a challenging task. In this paper, we introduce a novel approach, the feature adaptive fusion convolutional neural networks (CNN), which is designed to learn multiple 3D geological models with ore-controlling functions. The method is validated in the Axi epithermal gold deposit, northwestern China that mineralization distribution is jointly controlled by fault, volcanic phase, and phyllic alteration. The geology units are firstly constructed by explicit-implicit modeling and their ore-controlling features are subsequently described by high-frequency Laplace-Beltrami eigenfunctions and reassembled into multi-channel images as input to CNN. To learn the differences in ore-controlling effects among various geological units, we designed a fully connected layer to achieve adaptive quantification and weighted integration of the ore-controlling features by automatically optimizing weight allocation parameters and bias vectors using the neural network intelligence. Comparison results between the proposed method and other prospectivity methods suggest that the feature adaptive fusion CNN produces more reliable predictions, characterized by: (1) high consistency with known mineralization, (2) the highest AUC value and success rate, and (3) accurate prediction of deep voxels explored by drilling. Therefore, the proposed method effectively integrates the ore-controlling effects of multiple geological units and is suitable for complex scenarios of 3D MPM. Utilizing the prospectivity results generated by our method, we identified five potential mineralization in the Axi gold deposit, laying a robust foundation for future gold exploration.</div></div>","PeriodicalId":55973,"journal":{"name":"Chemie Der Erde-Geochemistry","volume":"84 4","pages":"Article 126190"},"PeriodicalIF":2.6000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"3D mineral prospectivity modeling at the Axi epithermal gold deposit, NW China by using a feature adaptive fusion strategy\",\"authors\":\"Xiancheng Mao, Jiaxuan Song, Zhankun Liu, Hao Deng, Jin Chen, Shuyan Yu, Yanan Wang, Ruike Xu, Yuanqian Nie, Yang Zheng\",\"doi\":\"10.1016/j.chemer.2024.126190\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Mineralization distribution is always intricately affected by multiple ore-controlling geological units that play different roles in a mineral system (e.g., driver, trap, and throttle). How to effectively balance and integrate ore-controlling features from various 3D geological models during 3D mineral prospectivity modeling (MPM) is still a challenging task. In this paper, we introduce a novel approach, the feature adaptive fusion convolutional neural networks (CNN), which is designed to learn multiple 3D geological models with ore-controlling functions. The method is validated in the Axi epithermal gold deposit, northwestern China that mineralization distribution is jointly controlled by fault, volcanic phase, and phyllic alteration. The geology units are firstly constructed by explicit-implicit modeling and their ore-controlling features are subsequently described by high-frequency Laplace-Beltrami eigenfunctions and reassembled into multi-channel images as input to CNN. To learn the differences in ore-controlling effects among various geological units, we designed a fully connected layer to achieve adaptive quantification and weighted integration of the ore-controlling features by automatically optimizing weight allocation parameters and bias vectors using the neural network intelligence. Comparison results between the proposed method and other prospectivity methods suggest that the feature adaptive fusion CNN produces more reliable predictions, characterized by: (1) high consistency with known mineralization, (2) the highest AUC value and success rate, and (3) accurate prediction of deep voxels explored by drilling. Therefore, the proposed method effectively integrates the ore-controlling effects of multiple geological units and is suitable for complex scenarios of 3D MPM. Utilizing the prospectivity results generated by our method, we identified five potential mineralization in the Axi gold deposit, laying a robust foundation for future gold exploration.</div></div>\",\"PeriodicalId\":55973,\"journal\":{\"name\":\"Chemie Der Erde-Geochemistry\",\"volume\":\"84 4\",\"pages\":\"Article 126190\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2024-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Chemie Der Erde-Geochemistry\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0009281924001156\",\"RegionNum\":3,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"GEOCHEMISTRY & GEOPHYSICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Chemie Der Erde-Geochemistry","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0009281924001156","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GEOCHEMISTRY & GEOPHYSICS","Score":null,"Total":0}
3D mineral prospectivity modeling at the Axi epithermal gold deposit, NW China by using a feature adaptive fusion strategy
Mineralization distribution is always intricately affected by multiple ore-controlling geological units that play different roles in a mineral system (e.g., driver, trap, and throttle). How to effectively balance and integrate ore-controlling features from various 3D geological models during 3D mineral prospectivity modeling (MPM) is still a challenging task. In this paper, we introduce a novel approach, the feature adaptive fusion convolutional neural networks (CNN), which is designed to learn multiple 3D geological models with ore-controlling functions. The method is validated in the Axi epithermal gold deposit, northwestern China that mineralization distribution is jointly controlled by fault, volcanic phase, and phyllic alteration. The geology units are firstly constructed by explicit-implicit modeling and their ore-controlling features are subsequently described by high-frequency Laplace-Beltrami eigenfunctions and reassembled into multi-channel images as input to CNN. To learn the differences in ore-controlling effects among various geological units, we designed a fully connected layer to achieve adaptive quantification and weighted integration of the ore-controlling features by automatically optimizing weight allocation parameters and bias vectors using the neural network intelligence. Comparison results between the proposed method and other prospectivity methods suggest that the feature adaptive fusion CNN produces more reliable predictions, characterized by: (1) high consistency with known mineralization, (2) the highest AUC value and success rate, and (3) accurate prediction of deep voxels explored by drilling. Therefore, the proposed method effectively integrates the ore-controlling effects of multiple geological units and is suitable for complex scenarios of 3D MPM. Utilizing the prospectivity results generated by our method, we identified five potential mineralization in the Axi gold deposit, laying a robust foundation for future gold exploration.
期刊介绍:
GEOCHEMISTRY was founded as Chemie der Erde 1914 in Jena, and, hence, is one of the oldest journals for geochemistry-related topics.
GEOCHEMISTRY (formerly Chemie der Erde / Geochemistry) publishes original research papers, short communications, reviews of selected topics, and high-class invited review articles addressed at broad geosciences audience. Publications dealing with interdisciplinary questions are particularly welcome. Young scientists are especially encouraged to submit their work. Contributions will be published exclusively in English. The journal, through very personalized consultation and its worldwide distribution, offers entry into the world of international scientific communication, and promotes interdisciplinary discussion on chemical problems in a broad spectrum of geosciences.
The following topics are covered by the expertise of the members of the editorial board (see below):
-cosmochemistry, meteoritics-
igneous, metamorphic, and sedimentary petrology-
volcanology-
low & high temperature geochemistry-
experimental - theoretical - field related studies-
mineralogy - crystallography-
environmental geosciences-
archaeometry