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

IF 2.6 3区 地球科学 Q2 GEOCHEMISTRY & GEOPHYSICS
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,&nbsp;Jiaxuan Song,&nbsp;Zhankun Liu,&nbsp;Hao Deng,&nbsp;Jin Chen,&nbsp;Shuyan Yu,&nbsp;Yanan Wang,&nbsp;Ruike Xu,&nbsp;Yuanqian Nie,&nbsp;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,&nbsp;Jiaxuan Song,&nbsp;Zhankun Liu,&nbsp;Hao Deng,&nbsp;Jin Chen,&nbsp;Shuyan Yu,&nbsp;Yanan Wang,&nbsp;Ruike Xu,&nbsp;Yuanqian Nie,&nbsp;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}
引用次数: 0

摘要

成矿分布总是受到多个控矿地质单元的复杂影响,这些控矿地质单元在成矿系统中起着不同的作用(如驱动、圈闭和节流)。在三维找矿建模(MPM)中,如何有效地平衡和整合各种三维地质模型的控矿特征仍然是一个具有挑战性的任务。本文介绍了一种新的方法——特征自适应融合卷积神经网络(CNN),该方法旨在学习具有控矿功能的多个三维地质模型。该方法在西北阿西浅成低温热液金矿床中得到验证,成矿分布受断裂、火山相和层序蚀变共同控制。首先通过显式-隐式建模构建地质单元,然后利用高频拉普拉斯-贝尔特拉米特征函数描述地质单元的控矿特征,并将其重组成多通道图像输入CNN。为了解不同地质单元控矿效果的差异性,设计了全连通层,利用神经网络智能自动优化权值分配参数和偏置向量,实现控矿特征的自适应量化和加权积分。与其他前瞻性方法的对比结果表明,特征自适应融合CNN预测更加可靠,具有以下特点:(1)与已知矿化一致性高,(2)AUC值和成功率最高,(3)对钻探勘探的深度体素预测准确。因此,该方法有效地综合了多个地质单元的控矿效果,适用于复杂的三维MPM场景。利用该方法的找矿结果,确定了阿西金矿床的5个潜在成矿作用,为下一步金矿找矿奠定了坚实的基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Chemie Der Erde-Geochemistry
Chemie Der Erde-Geochemistry 地学-地球化学与地球物理
CiteScore
7.10
自引率
0.00%
发文量
40
审稿时长
3.0 months
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
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信