Jianping Zhou, Ehsan Farahbakhsh, Simon Williams, Xiaohui Li, Yongjiang Liu, Sanzhong Li, R. Dietmar Müller
{"title":"机器学习和大数据挖掘揭示地球深时地壳厚度和构造演化:一种新的化学同质学方法","authors":"Jianping Zhou, Ehsan Farahbakhsh, Simon Williams, Xiaohui Li, Yongjiang Liu, Sanzhong Li, R. Dietmar Müller","doi":"10.1029/2024JB030404","DOIUrl":null,"url":null,"abstract":"<p>Quantitative analysis of crustal thickness evolution across deep time poses critical insights into the planet's geological history. It may help uncover new areas with potential critical mineral deposits and reveal the impacts of crustal thickness and elevation changes on the development of the atmosphere, hydrosphere, and biosphere. However, most existing estimation methods are restricted to arc-related magmas, limiting their broader application. By mining extensive geochemical data from present-day subduction zones, collision orogenic belts, and non-subduction-related intraplate igneous rock samples worldwide, along with their corresponding Moho depths during magmatism, we have developed a machine learning-based mohometry linking geochemical data to Moho depth, which is universally applicable in reconstructing ancient orogenic systems' paleo-crustal evolution and tracking complex tectonic histories in both spatial and temporal dimensions. Our novel mohometry model demonstrates robust performance, achieving an <i>R</i><sup>2</sup> of 0.937 and an Root Mean Squared Error of 4.3 km. Feature importance filtering highlights key geochemical proxies, allowing for accurate paleo-crustal thickness estimation even when many elements are missing. Model validation in southern Tibet and the South China Block, regions characterized by well-constrained crustal histories and complex tectonic processes, demonstrates its broad applicability. Reconstructed paleo-crustal thickness records reveal a strong correlation between crustal thickening events and the formation of porphyry ore deposits, offering new insights for mineral exploration in ancient orogens subjected to significant surface erosion. By enabling the reconstruction of crustal thickness across geological timescales, this model enhances our understanding of Earth's internal dynamics and their interactions with surface processes, thereby advancing our comprehension of Earth's geological history.</p>","PeriodicalId":15864,"journal":{"name":"Journal of Geophysical Research: Solid Earth","volume":"130 5","pages":""},"PeriodicalIF":4.1000,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine Learning and Big Data Mining Reveal Earth's Deep Time Crustal Thickness and Tectonic Evolution: A New Chemical Mohometry Approach\",\"authors\":\"Jianping Zhou, Ehsan Farahbakhsh, Simon Williams, Xiaohui Li, Yongjiang Liu, Sanzhong Li, R. Dietmar Müller\",\"doi\":\"10.1029/2024JB030404\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Quantitative analysis of crustal thickness evolution across deep time poses critical insights into the planet's geological history. It may help uncover new areas with potential critical mineral deposits and reveal the impacts of crustal thickness and elevation changes on the development of the atmosphere, hydrosphere, and biosphere. However, most existing estimation methods are restricted to arc-related magmas, limiting their broader application. By mining extensive geochemical data from present-day subduction zones, collision orogenic belts, and non-subduction-related intraplate igneous rock samples worldwide, along with their corresponding Moho depths during magmatism, we have developed a machine learning-based mohometry linking geochemical data to Moho depth, which is universally applicable in reconstructing ancient orogenic systems' paleo-crustal evolution and tracking complex tectonic histories in both spatial and temporal dimensions. Our novel mohometry model demonstrates robust performance, achieving an <i>R</i><sup>2</sup> of 0.937 and an Root Mean Squared Error of 4.3 km. Feature importance filtering highlights key geochemical proxies, allowing for accurate paleo-crustal thickness estimation even when many elements are missing. Model validation in southern Tibet and the South China Block, regions characterized by well-constrained crustal histories and complex tectonic processes, demonstrates its broad applicability. Reconstructed paleo-crustal thickness records reveal a strong correlation between crustal thickening events and the formation of porphyry ore deposits, offering new insights for mineral exploration in ancient orogens subjected to significant surface erosion. By enabling the reconstruction of crustal thickness across geological timescales, this model enhances our understanding of Earth's internal dynamics and their interactions with surface processes, thereby advancing our comprehension of Earth's geological history.</p>\",\"PeriodicalId\":15864,\"journal\":{\"name\":\"Journal of Geophysical Research: Solid Earth\",\"volume\":\"130 5\",\"pages\":\"\"},\"PeriodicalIF\":4.1000,\"publicationDate\":\"2025-05-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Geophysical Research: Solid Earth\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1029/2024JB030404\",\"RegionNum\":2,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"GEOCHEMISTRY & GEOPHYSICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Geophysical Research: Solid Earth","FirstCategoryId":"89","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1029/2024JB030404","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOCHEMISTRY & GEOPHYSICS","Score":null,"Total":0}
Machine Learning and Big Data Mining Reveal Earth's Deep Time Crustal Thickness and Tectonic Evolution: A New Chemical Mohometry Approach
Quantitative analysis of crustal thickness evolution across deep time poses critical insights into the planet's geological history. It may help uncover new areas with potential critical mineral deposits and reveal the impacts of crustal thickness and elevation changes on the development of the atmosphere, hydrosphere, and biosphere. However, most existing estimation methods are restricted to arc-related magmas, limiting their broader application. By mining extensive geochemical data from present-day subduction zones, collision orogenic belts, and non-subduction-related intraplate igneous rock samples worldwide, along with their corresponding Moho depths during magmatism, we have developed a machine learning-based mohometry linking geochemical data to Moho depth, which is universally applicable in reconstructing ancient orogenic systems' paleo-crustal evolution and tracking complex tectonic histories in both spatial and temporal dimensions. Our novel mohometry model demonstrates robust performance, achieving an R2 of 0.937 and an Root Mean Squared Error of 4.3 km. Feature importance filtering highlights key geochemical proxies, allowing for accurate paleo-crustal thickness estimation even when many elements are missing. Model validation in southern Tibet and the South China Block, regions characterized by well-constrained crustal histories and complex tectonic processes, demonstrates its broad applicability. Reconstructed paleo-crustal thickness records reveal a strong correlation between crustal thickening events and the formation of porphyry ore deposits, offering new insights for mineral exploration in ancient orogens subjected to significant surface erosion. By enabling the reconstruction of crustal thickness across geological timescales, this model enhances our understanding of Earth's internal dynamics and their interactions with surface processes, thereby advancing our comprehension of Earth's geological history.
期刊介绍:
The Journal of Geophysical Research: Solid Earth serves as the premier publication for the breadth of solid Earth geophysics including (in alphabetical order): electromagnetic methods; exploration geophysics; geodesy and gravity; geodynamics, rheology, and plate kinematics; geomagnetism and paleomagnetism; hydrogeophysics; Instruments, techniques, and models; solid Earth interactions with the cryosphere, atmosphere, oceans, and climate; marine geology and geophysics; natural and anthropogenic hazards; near surface geophysics; petrology, geochemistry, and mineralogy; planet Earth physics and chemistry; rock mechanics and deformation; seismology; tectonophysics; and volcanology.
JGR: Solid Earth has long distinguished itself as the venue for publication of Research Articles backed solidly by data and as well as presenting theoretical and numerical developments with broad applications. Research Articles published in JGR: Solid Earth have had long-term impacts in their fields.
JGR: Solid Earth provides a venue for special issues and special themes based on conferences, workshops, and community initiatives. JGR: Solid Earth also publishes Commentaries on research and emerging trends in the field; these are commissioned by the editors, and suggestion are welcome.