用于绘制矿产远景图的可解释人工智能模型

IF 6 2区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY
Renguang Zuo, Qiuming Cheng, Ying Xu, Fanfan Yang, Yihui Xiong, Ziye Wang, Oliver P. Kreuzer
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引用次数: 0

摘要

矿产远景测绘(MPM)旨在通过组合和分析地质找矿大数据来缩小勘探搜索空间。此类地质大数据过于庞大和复杂,人类无法有效处理和解释。人工智能(AI)算法是在矿产勘探大数据中挖掘非线性成矿模式的有力工具,在 MPM 中表现出了卓越的性能。然而,人工智能驱动的 MPM 面临着一些挑战,包括难以解释、普适性差和物理不一致性等。在本研究中,我们在以往研究的基础上设计了一种新的工作流程,旨在通过将领域知识嵌入人工智能驱动的 MPM(从输入数据到模型设计和模型输出),为 MPM 构建更加透明和可解释的人工智能(XAI)模型。这种新提出的方法提供了强大的地质和概念线索,指导整个人工智能驱动的 MPM 模型训练过程,从而提高了模型的可解释性和性能。总体而言,为 MPM 开发 XAI 模型能够在整个建模过程中嵌入先验知识和专家知识,为旨在改进 MPM 的未来研究提供了一个有价值、有前景的领域。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Explainable artificial intelligence models for mineral prospectivity mapping

Mineral prospectivity mapping (MPM) is designed to reduce the exploration search space by combining and analyzing geological prospecting big data. Such geological big data are too large and complex for humans to effectively handle and interpret. Artificial intelligence (AI) algorithms, which are powerful tools for mining nonlinear mineralization patterns in big data obtained from mineral exploration, have demonstrated excellent performance in MPM. However, AI-driven MPM faces several challenges, including difficult interpretability, poor generalizability, and physical inconsistencies. In this study, based on previous studies, we devised a novel workflow that aims to constructing more transparent and explainable artificial intelligence (XAI) models for MPM by embedding domain knowledge throughout the AI-driven MPM, from input data to model design and model output. This newly proposed approach provides strong geological and conceptual leads that guide the entire AI-driven MPM model training process, thereby improving model interpretability and performance. Overall, the development of XAI models for MPM is capable of embedding prior and expert knowledge throughout the modeling process, presenting a valuable and promising area for future research designed to improve MPM.

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来源期刊
Science China Earth Sciences
Science China Earth Sciences GEOSCIENCES, MULTIDISCIPLINARY-
CiteScore
9.60
自引率
5.30%
发文量
135
审稿时长
3-8 weeks
期刊介绍: Science China Earth Sciences, an academic journal cosponsored by the Chinese Academy of Sciences and the National Natural Science Foundation of China, and published by Science China Press, is committed to publishing high-quality, original results in both basic and applied research.
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