Extracting the geochemical characteristics of magmas in different global tectono-magmatic settings using sparse modeling
In this study, key geochemical features of magmas formed in eight different tectono-magmatic settings (mid-ocean ridges, oceanic islands, oceanic plateaus, continental flood basalt provinces, intra-oceanic arcs, continental arcs, island arcs, and back-arc basins) are presented that were obtained usi...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-10-01
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| Series: | Frontiers in Earth Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2022.994580/full |
| _version_ | 1811195531760762880 |
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| author | Kenta Ueki Hideitsu Hino Tatsu Kuwatani |
| author_facet | Kenta Ueki Hideitsu Hino Tatsu Kuwatani |
| author_sort | Kenta Ueki |
| collection | DOAJ |
| description | In this study, key geochemical features of magmas formed in eight different tectono-magmatic settings (mid-ocean ridges, oceanic islands, oceanic plateaus, continental flood basalt provinces, intra-oceanic arcs, continental arcs, island arcs, and back-arc basins) are presented that were obtained using a machine-learning-based statistical model. We analyzed geochemical data for volcanic rocks compiled from the global geochemical databases based on statistical model fitting. We used the sparse modeling approach, with which we can objectively identify a small number of fundamental features from a large number of observations. This approach allowed us to identify a small number of representative geochemical features from a total of 857 variables, including major and trace element concentrations, isotope ratios, and all possible ratios and multiplications of elements. Based on the statistical analysis, we present a small number (2–4) of key geochemical features for each tectono-magmatic setting. The extracted geochemical features and associated diagrams can be used to examine geochemical similarities and differences between tectono-magmatic settings and to identify the geochemical characteristics of unknown samples. Based on the extracted geochemical characteristics, we discuss the processes that may lead to the formation of magmas in different tectono-magmatic settings. Our statistical analysis shows that the geochemical signatures of magmas vary with the tectono-magmatic setting, as do the geochemical processes involved in magma generation. |
| first_indexed | 2024-04-12T00:44:46Z |
| format | Article |
| id | doaj.art-4d13bd33256f4ebdbd9cf2f9b5c6015b |
| institution | Directory Open Access Journal |
| issn | 2296-6463 |
| language | English |
| last_indexed | 2024-04-12T00:44:46Z |
| publishDate | 2022-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Earth Science |
| spelling | doaj.art-4d13bd33256f4ebdbd9cf2f9b5c6015b2022-12-22T03:54:54ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632022-10-011010.3389/feart.2022.994580994580Extracting the geochemical characteristics of magmas in different global tectono-magmatic settings using sparse modelingKenta Ueki0Hideitsu Hino1Tatsu Kuwatani2Research Institute for Marine Geodynamics, Japan Agency for Marine-Earth Science and Technology, Yokosuka, Kanagawa, JapanThe Institute of Statistical Mathematics, Tachikawa, Tokyo, JapanResearch Institute for Marine Geodynamics, Japan Agency for Marine-Earth Science and Technology, Yokosuka, Kanagawa, JapanIn this study, key geochemical features of magmas formed in eight different tectono-magmatic settings (mid-ocean ridges, oceanic islands, oceanic plateaus, continental flood basalt provinces, intra-oceanic arcs, continental arcs, island arcs, and back-arc basins) are presented that were obtained using a machine-learning-based statistical model. We analyzed geochemical data for volcanic rocks compiled from the global geochemical databases based on statistical model fitting. We used the sparse modeling approach, with which we can objectively identify a small number of fundamental features from a large number of observations. This approach allowed us to identify a small number of representative geochemical features from a total of 857 variables, including major and trace element concentrations, isotope ratios, and all possible ratios and multiplications of elements. Based on the statistical analysis, we present a small number (2–4) of key geochemical features for each tectono-magmatic setting. The extracted geochemical features and associated diagrams can be used to examine geochemical similarities and differences between tectono-magmatic settings and to identify the geochemical characteristics of unknown samples. Based on the extracted geochemical characteristics, we discuss the processes that may lead to the formation of magmas in different tectono-magmatic settings. Our statistical analysis shows that the geochemical signatures of magmas vary with the tectono-magmatic setting, as do the geochemical processes involved in magma generation.https://www.frontiersin.org/articles/10.3389/feart.2022.994580/fulltectono-magmatic settingsmachine learninggeochemical features of magmasmagma generation processesfeature extraction |
| spellingShingle | Kenta Ueki Hideitsu Hino Tatsu Kuwatani Extracting the geochemical characteristics of magmas in different global tectono-magmatic settings using sparse modeling Frontiers in Earth Science tectono-magmatic settings machine learning geochemical features of magmas magma generation processes feature extraction |
| title | Extracting the geochemical characteristics of magmas in different global tectono-magmatic settings using sparse modeling |
| title_full | Extracting the geochemical characteristics of magmas in different global tectono-magmatic settings using sparse modeling |
| title_fullStr | Extracting the geochemical characteristics of magmas in different global tectono-magmatic settings using sparse modeling |
| title_full_unstemmed | Extracting the geochemical characteristics of magmas in different global tectono-magmatic settings using sparse modeling |
| title_short | Extracting the geochemical characteristics of magmas in different global tectono-magmatic settings using sparse modeling |
| title_sort | extracting the geochemical characteristics of magmas in different global tectono magmatic settings using sparse modeling |
| topic | tectono-magmatic settings machine learning geochemical features of magmas magma generation processes feature extraction |
| url | https://www.frontiersin.org/articles/10.3389/feart.2022.994580/full |
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