Insights on source lithology and pressure-temperature conditions of basalt generation using machine learning
Identifying the origin and conditions of basalt generation is a crucial yet formidable task. To tackle this challenge, we introduce an innovative approach leveraging machine learning. Our methodology relies on a comprehensive database of approximately one thousand major element concentrations derive...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2024
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| Online Access: | https://hdl.handle.net/10356/181733 |
| _version_ | 1826128996851515392 |
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| author | Cheng, Lilu Yang, Zongfeng Costa, Fidel |
| author2 | Asian School of the Environment |
| author_facet | Asian School of the Environment Cheng, Lilu Yang, Zongfeng Costa, Fidel |
| author_sort | Cheng, Lilu |
| collection | NTU |
| description | Identifying the origin and conditions of basalt generation is a crucial yet formidable task. To tackle this challenge, we introduce an innovative approach leveraging machine learning. Our methodology relies on a comprehensive database of approximately one thousand major element concentrations derived from glass samples generated through experiments encompassing a wide range of source lithologies, pressure (from 0.28 to 20 GPa) and temperature (850–2100°C). We first applied the XGBoost classification models to assess the compositional characteristics of melts from three principal mantle source categories: peridotitic, transitional, and mafic sources. We obtained an accuracy of approximately 96% on the test data set. Furthermore, we also employ an XGBoost regression model to predict the pressure and temperature conditions of generation of basalts from diverse lithologic sources. Our predictions of temperature and pressure exhibit remarkable precisions, of about 49°C and 0.37 GPa, respectively. To enhance accessibility of our model, we have implemented a user-friendly web browser application, available at (https://huggingface.co/spaces/lilucheng/sourcedetection). The web application allows users to swiftly recover the source lithology as well as pressure and temperature conditions governing basalt generation for a broad array of samples within a matter of seconds. |
| first_indexed | 2025-03-09T14:58:42Z |
| format | Journal Article |
| id | ntu-10356/181733 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-03-09T14:58:42Z |
| publishDate | 2024 |
| record_format | dspace |
| spelling | ntu-10356/1817332024-12-16T15:30:53Z Insights on source lithology and pressure-temperature conditions of basalt generation using machine learning Cheng, Lilu Yang, Zongfeng Costa, Fidel Asian School of the Environment Real Estate Analytics, Singapore Earth Observatory of Singapore Earth and Environmental Sciences Lithology Machine learning Identifying the origin and conditions of basalt generation is a crucial yet formidable task. To tackle this challenge, we introduce an innovative approach leveraging machine learning. Our methodology relies on a comprehensive database of approximately one thousand major element concentrations derived from glass samples generated through experiments encompassing a wide range of source lithologies, pressure (from 0.28 to 20 GPa) and temperature (850–2100°C). We first applied the XGBoost classification models to assess the compositional characteristics of melts from three principal mantle source categories: peridotitic, transitional, and mafic sources. We obtained an accuracy of approximately 96% on the test data set. Furthermore, we also employ an XGBoost regression model to predict the pressure and temperature conditions of generation of basalts from diverse lithologic sources. Our predictions of temperature and pressure exhibit remarkable precisions, of about 49°C and 0.37 GPa, respectively. To enhance accessibility of our model, we have implemented a user-friendly web browser application, available at (https://huggingface.co/spaces/lilucheng/sourcedetection). The web application allows users to swiftly recover the source lithology as well as pressure and temperature conditions governing basalt generation for a broad array of samples within a matter of seconds. Ministry of Education (MOE) National Research Foundation (NRF) Published version This research was supported by the National Research Foundation of Singapore and the Singapore Ministry of Education under the Research Centres of Excellence initiative, and a National Research Foundation Singapore Investigatorship Award (NRF‐NRFI2017‐06). FC is also funded by the project “Chaire d’Excellence” of Universite de Paris Cite. ZF is funded by the National Science Foundation of China (41402053). 2024-12-16T05:55:36Z 2024-12-16T05:55:36Z 2024 Journal Article Cheng, L., Yang, Z. & Costa, F. (2024). Insights on source lithology and pressure-temperature conditions of basalt generation using machine learning. Earth and Space Science, 11(7). https://dx.doi.org/10.1029/2024EA003732 2333-5084 https://hdl.handle.net/10356/181733 10.1029/2024EA003732 2-s2.0-85197716558 7 11 en NRF‐NRFI2017‐06 Earth and Space Science © 2024 The Author(s). This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. application/pdf |
| spellingShingle | Earth and Environmental Sciences Lithology Machine learning Cheng, Lilu Yang, Zongfeng Costa, Fidel Insights on source lithology and pressure-temperature conditions of basalt generation using machine learning |
| title | Insights on source lithology and pressure-temperature conditions of basalt generation using machine learning |
| title_full | Insights on source lithology and pressure-temperature conditions of basalt generation using machine learning |
| title_fullStr | Insights on source lithology and pressure-temperature conditions of basalt generation using machine learning |
| title_full_unstemmed | Insights on source lithology and pressure-temperature conditions of basalt generation using machine learning |
| title_short | Insights on source lithology and pressure-temperature conditions of basalt generation using machine learning |
| title_sort | insights on source lithology and pressure temperature conditions of basalt generation using machine learning |
| topic | Earth and Environmental Sciences Lithology Machine learning |
| url | https://hdl.handle.net/10356/181733 |
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