Explainable artificial intelligence models for mineral prospectivity mapping.
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| Title: | Explainable artificial intelligence models for mineral prospectivity mapping. |
|---|---|
| Authors: | Zuo, Renguang1 zrguang@cug.edu.cn, Cheng, Qiuming1,2, Xu, Ying1, Yang, Fanfan1, Xiong, Yihui1, Wang, Ziye1, Kreuzer, Oliver P.3,4 |
| Source: | SCIENCE CHINA Earth Sciences. Sep2024, Vol. 67 Issue 9, p2864-2875. 12p. |
| Subjects: | Artificial intelligence, Computer input design, Prospecting, Space exploration, Minerals |
| Abstract: | 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. [ABSTRACT FROM AUTHOR] |
| Copyright of SCIENCE CHINA Earth Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 179086586 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Explainable artificial intelligence models for mineral prospectivity mapping. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zuo%2C+Renguang%22">Zuo, Renguang</searchLink><relatesTo>1</relatesTo><i> zrguang@cug.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Cheng%2C+Qiuming%22">Cheng, Qiuming</searchLink><relatesTo>1,2</relatesTo><br /><searchLink fieldCode="AR" term="%22Xu%2C+Ying%22">Xu, Ying</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Yang%2C+Fanfan%22">Yang, Fanfan</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Xiong%2C+Yihui%22">Xiong, Yihui</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Wang%2C+Ziye%22">Wang, Ziye</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Kreuzer%2C+Oliver+P%2E%22">Kreuzer, Oliver P.</searchLink><relatesTo>3,4</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22SCIENCE+CHINA+Earth+Sciences%22">SCIENCE CHINA Earth Sciences</searchLink>. Sep2024, Vol. 67 Issue 9, p2864-2875. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+input+design%22">Computer input design</searchLink><br /><searchLink fieldCode="DE" term="%22Prospecting%22">Prospecting</searchLink><br /><searchLink fieldCode="DE" term="%22Space+exploration%22">Space exploration</searchLink><br /><searchLink fieldCode="DE" term="%22Minerals%22">Minerals</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of SCIENCE CHINA Earth Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11430-024-1309-9 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 2864 Subjects: – SubjectFull: Artificial intelligence Type: general – SubjectFull: Computer input design Type: general – SubjectFull: Prospecting Type: general – SubjectFull: Space exploration Type: general – SubjectFull: Minerals Type: general Titles: – TitleFull: Explainable artificial intelligence models for mineral prospectivity mapping. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zuo, Renguang – PersonEntity: Name: NameFull: Cheng, Qiuming – PersonEntity: Name: NameFull: Xu, Ying – PersonEntity: Name: NameFull: Yang, Fanfan – PersonEntity: Name: NameFull: Xiong, Yihui – PersonEntity: Name: NameFull: Wang, Ziye – PersonEntity: Name: NameFull: Kreuzer, Oliver P. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 16747313 Numbering: – Type: volume Value: 67 – Type: issue Value: 9 Titles: – TitleFull: SCIENCE CHINA Earth Sciences Type: main |
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