Machine Learning Reveals Source Compositions of Intraplate Basaltic Rocks
Abstract Recycling of crustal material is thought to introduce pyroxenite to the peridotite mantle. Mapping such lithological heterogeneity within the mantle is crucial to understanding the mantle's chemical evolution but remains challenging. By sampling the mantle source, intraplate basaltic m...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-09-01
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| Series: | Geochemistry, Geophysics, Geosystems |
| Online Access: | https://doi.org/10.1029/2021GC009946 |
| _version_ | 1797638076342730752 |
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| author | Peng Guo Ting Yang Wen‐Liang Xu Bin Chen |
| author_facet | Peng Guo Ting Yang Wen‐Liang Xu Bin Chen |
| author_sort | Peng Guo |
| collection | DOAJ |
| description | Abstract Recycling of crustal material is thought to introduce pyroxenite to the peridotite mantle. Mapping such lithological heterogeneity within the mantle is crucial to understanding the mantle's chemical evolution but remains challenging. By sampling the mantle source, intraplate basaltic melts provide a unique chance to reveal lithological heterogeneity within the mantle. We train machine learning (ML) models with major oxide data of experimental peridotite and pyroxenite melts to help reveal the mantle source lithology of basaltic rocks. The ML models can predict source lithologies from major oxide information with an accuracy larger than 94%. As a case study, we predict source lithology of the Cenozoic intraplate basaltic rocks in Northeast China. Our ML models suggest that pyroxenite dominates the mantle source of basaltic rocks sitting above the stagnant Pacific slab while peridotite dominates the source of the basaltic rocks located west of the slab tip, consistent with previous studies using other approaches. Our ML models could potentially be used to infer mantle source lithologies of basaltic rocks from other regions around the world. |
| first_indexed | 2024-03-11T12:58:27Z |
| format | Article |
| id | doaj.art-549cd81934934f94818677db60336ac4 |
| institution | Directory Open Access Journal |
| issn | 1525-2027 |
| language | English |
| last_indexed | 2024-03-11T12:58:27Z |
| publishDate | 2021-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geochemistry, Geophysics, Geosystems |
| spelling | doaj.art-549cd81934934f94818677db60336ac42023-11-03T16:55:50ZengWileyGeochemistry, Geophysics, Geosystems1525-20272021-09-01229n/an/a10.1029/2021GC009946Machine Learning Reveals Source Compositions of Intraplate Basaltic RocksPeng Guo0Ting Yang1Wen‐Liang Xu2Bin Chen3Department of Earth and Space Sciences Southern University of Science and Technology Shenzhen ChinaDepartment of Earth and Space Sciences Southern University of Science and Technology Shenzhen ChinaCollege of Earth Sciences Jilin University Changchun ChinaDepartment of Earth and Space Sciences Southern University of Science and Technology Shenzhen ChinaAbstract Recycling of crustal material is thought to introduce pyroxenite to the peridotite mantle. Mapping such lithological heterogeneity within the mantle is crucial to understanding the mantle's chemical evolution but remains challenging. By sampling the mantle source, intraplate basaltic melts provide a unique chance to reveal lithological heterogeneity within the mantle. We train machine learning (ML) models with major oxide data of experimental peridotite and pyroxenite melts to help reveal the mantle source lithology of basaltic rocks. The ML models can predict source lithologies from major oxide information with an accuracy larger than 94%. As a case study, we predict source lithology of the Cenozoic intraplate basaltic rocks in Northeast China. Our ML models suggest that pyroxenite dominates the mantle source of basaltic rocks sitting above the stagnant Pacific slab while peridotite dominates the source of the basaltic rocks located west of the slab tip, consistent with previous studies using other approaches. Our ML models could potentially be used to infer mantle source lithologies of basaltic rocks from other regions around the world.https://doi.org/10.1029/2021GC009946 |
| spellingShingle | Peng Guo Ting Yang Wen‐Liang Xu Bin Chen Machine Learning Reveals Source Compositions of Intraplate Basaltic Rocks Geochemistry, Geophysics, Geosystems |
| title | Machine Learning Reveals Source Compositions of Intraplate Basaltic Rocks |
| title_full | Machine Learning Reveals Source Compositions of Intraplate Basaltic Rocks |
| title_fullStr | Machine Learning Reveals Source Compositions of Intraplate Basaltic Rocks |
| title_full_unstemmed | Machine Learning Reveals Source Compositions of Intraplate Basaltic Rocks |
| title_short | Machine Learning Reveals Source Compositions of Intraplate Basaltic Rocks |
| title_sort | machine learning reveals source compositions of intraplate basaltic rocks |
| url | https://doi.org/10.1029/2021GC009946 |
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