1
|
Mather B, Seton M, Williams S, Whittaker J, Carey R, Arnould M, Coltice N, Duncan R. Spreading ridge migration enabled by plume-ridge de-anchoring. Nat Commun 2024; 15:8934. [PMID: 39414825 PMCID: PMC11484986 DOI: 10.1038/s41467-024-53397-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Accepted: 10/11/2024] [Indexed: 10/18/2024] Open
Abstract
It has long been recognised that spreading ridges are kept in place by competing subduction forces that drive plate motions. Asymmetric strain rates pull spreading ridges in the direction of the strongest slab pull force, which partially explains why spreading ridges can migrate vast distances. However, the interaction between mantle plumes and spreading ridges plays a relatively unknown role on the evolution of plate boundaries. Using a numerical model of mantle convection, we show that plumes with high buoyancy flux (>3000 kg/s) can capture spreading ridges within a 1000 km radius and anchor them in place. Exceptionally high buoyancy fluxes may fragment the overriding plate into smaller plates to accommodate more efficient plate motion. If the plume buoyancy flux wanes below 1000 kg/s the ridge may be de-anchored, leading to rapid ridge migration rates when combined with asymmetric plate boundary forces. Our results show that plume-ridge de-anchoring may have contributed to the rapid migration of the SE Indian Ridge from 43 million years ago (Ma) due to waning buoyancy flux from the Kerguelen plume, supported by magma flux estimates and radiogenic isotope geochemistry of eruption products. The plume-ridge de-anchoring mechanism we have identified has global implications for the evolution of plate boundaries near mantle plumes.
Collapse
Affiliation(s)
- Ben Mather
- EarthByte Group, School of Geosciences, The University of Sydney, Sydney, NSW, Australia.
| | - Maria Seton
- EarthByte Group, School of Geosciences, The University of Sydney, Sydney, NSW, Australia
| | - Simon Williams
- Institute of Marine and Antarctic Studies, University of Tasmania, Hobart, TAS, Australia
| | - Joanne Whittaker
- Institute of Marine and Antarctic Studies, University of Tasmania, Hobart, TAS, Australia
| | - Rebecca Carey
- CODES School of Natural Sciences, University of Tasmania, Hobart, TAS, Australia
| | - Maëlis Arnould
- Laboratoire de Géologie de Lyon - Terre, Planètes, Environnement, LGL-TPE, University of Lyon, UCBL, ENSL, UJM, CNRS, Villeurbanne, France
| | - Nicolas Coltice
- Observatoire de la Côte d'Azur, Université Côte d'Azur, CNRS, IRD, Géoazur, Valbonne, France
| | - Robert Duncan
- College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis, OR, USA
| |
Collapse
|
2
|
Zhao T, Wang S, Ouyang C, Chen M, Liu C, Zhang J, Yu L, Wang F, Xie Y, Li J, Wang F, Grunwald S, Wong BM, Zhang F, Qian Z, Xu Y, Yu C, Han W, Sun T, Shao Z, Qian T, Chen Z, Zeng J, Zhang H, Letu H, Zhang B, Wang L, Luo L, Shi C, Su H, Zhang H, Yin S, Huang N, Zhao W, Li N, Zheng C, Zhou Y, Huang C, Feng D, Xu Q, Wu Y, Hong D, Wang Z, Lin Y, Zhang T, Kumar P, Plaza A, Chanussot J, Zhang J, Shi J, Wang L. Artificial intelligence for geoscience: Progress, challenges, and perspectives. Innovation (N Y) 2024; 5:100691. [PMID: 39285902 PMCID: PMC11404188 DOI: 10.1016/j.xinn.2024.100691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 08/17/2024] [Indexed: 09/19/2024] Open
Abstract
This paper explores the evolution of geoscientific inquiry, tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence (AI) and data collection techniques. Traditional models, which are grounded in physical and numerical frameworks, provide robust explanations by explicitly reconstructing underlying physical processes. However, their limitations in comprehensively capturing Earth's complexities and uncertainties pose challenges in optimization and real-world applicability. In contrast, contemporary data-driven models, particularly those utilizing machine learning (ML) and deep learning (DL), leverage extensive geoscience data to glean insights without requiring exhaustive theoretical knowledge. ML techniques have shown promise in addressing Earth science-related questions. Nevertheless, challenges such as data scarcity, computational demands, data privacy concerns, and the "black-box" nature of AI models hinder their seamless integration into geoscience. The integration of physics-based and data-driven methodologies into hybrid models presents an alternative paradigm. These models, which incorporate domain knowledge to guide AI methodologies, demonstrate enhanced efficiency and performance with reduced training data requirements. This review provides a comprehensive overview of geoscientific research paradigms, emphasizing untapped opportunities at the intersection of advanced AI techniques and geoscience. It examines major methodologies, showcases advances in large-scale models, and discusses the challenges and prospects that will shape the future landscape of AI in geoscience. The paper outlines a dynamic field ripe with possibilities, poised to unlock new understandings of Earth's complexities and further advance geoscience exploration.
Collapse
Affiliation(s)
- Tianjie Zhao
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
| | - Sheng Wang
- School of Computer Science, China University of Geosciences, Wuhan 430078, China
| | - Chaojun Ouyang
- State Key Laboratory of Mountain Hazards and Engineering Resilience, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610299, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Min Chen
- Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University, Nanjing 210023, China
| | - Chenying Liu
- Data Science in Earth Observation, Technical University of Munich, 80333 Munich, Germany
| | - Jin Zhang
- The National Key Laboratory of Water Disaster Prevention, Yangtze Institute for Conservation and Development, Hohai University, Nanjing 210098, China
| | - Long Yu
- School of Computer Science, China University of Geosciences, Wuhan 430078, China
| | - Fei Wang
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yong Xie
- School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Jun Li
- School of Computer Science, China University of Geosciences, Wuhan 430078, China
| | - Fang Wang
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Department of Chemistry, Technical University of Munich, 85748 Munich, Germany
| | - Sabine Grunwald
- Soil, Water and Ecosystem Sciences Department, University of Florida, PO Box 110290, Gainesville, FL, USA
| | - Bryan M Wong
- Materials Science Engineering Program Cooperating Faculty Member in the Department of Chemistry and Department of Physics Astronomy, University of California, California, Riverside, CA 92521, USA
| | - Fan Zhang
- Institute of Remote Sensing and Geographical Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China
| | - Zhen Qian
- Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University, Nanjing 210023, China
| | - Yongjun Xu
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Chengqing Yu
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wei Han
- School of Computer Science, China University of Geosciences, Wuhan 430078, China
| | - Tao Sun
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
| | - Zezhi Shao
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Tangwen Qian
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhao Chen
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
| | - Jiangyuan Zeng
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
| | - Huai Zhang
- Key Laboratory of Computational Geodynamics, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Husi Letu
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
| | - Bing Zhang
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
| | - Li Wang
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
| | - Lei Luo
- International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
| | - Chong Shi
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
| | - Hongjun Su
- College of Geography and Remote Sensing, Hohai University, Nanjing 211100, China
| | - Hongsheng Zhang
- Department of Geography, The University of Hong Kong, Hong Kong 999077, SAR, China
| | - Shuai Yin
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
| | - Ni Huang
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
| | - Wei Zhao
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
| | - Nan Li
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Nanjing 210044, China
- School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China
| | - Chaolei Zheng
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
| | - Yang Zhou
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory of Meteorological Disaster, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Changping Huang
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
| | - Defeng Feng
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Qingsong Xu
- Data Science in Earth Observation, Technical University of Munich, 80333 Munich, Germany
| | - Yan Wu
- Key Laboratory of Vertebrate Evolution and Human Origins of Chinese Academy of Sciences, Institute of Vertebrate Paleontology and Paleoanthropology, Chinese Academy of Sciences, Beijing 100044, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Danfeng Hong
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhenyu Wang
- Department of Catchment Hydrology, Helmholtz Centre for Environmental Research - UFZ, Halle (Saale) 06108, Germany
| | - Yinyi Lin
- Department of Geography, The University of Hong Kong, Hong Kong 999077, SAR, China
| | - Tangtang Zhang
- Key Laboratory of Land Surface Process and Climate Change in Cold and Arid Regions, Chinese Academy of Sciences, Lanzhou 730000, China
| | - Prashant Kumar
- Global Centre for Clean Air Research (GCARE), School of Sustainability, Civil and Environmental Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford GU2 7XH, UK
- Institute for Sustainability, University of Surrey, Guildford GU2 7XH, Surrey, UK
| | - Antonio Plaza
- Hyperspectral Computing Laboratory, University of Extremadura, 10003 Caceres, Spain
| | - Jocelyn Chanussot
- University Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, 38000 Grenoble, France
| | - Jiabao Zhang
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jiancheng Shi
- National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China
| | - Lizhe Wang
- School of Computer Science, China University of Geosciences, Wuhan 430078, China
| |
Collapse
|
7
|
Wang C, Hazen RM, Cheng Q, Stephenson MH, Zhou C, Fox P, Shen SZ, Oberhänsli R, Hou Z, Ma X, Feng Z, Fan J, Ma C, Hu X, Luo B, Wang J, Schiffries CM. The Deep-Time Digital Earth program: data-driven discovery in geosciences. Natl Sci Rev 2021; 8:nwab027. [PMID: 34691735 PMCID: PMC8433093 DOI: 10.1093/nsr/nwab027] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 02/03/2021] [Accepted: 02/05/2021] [Indexed: 11/29/2022] Open
Abstract
Current barriers hindering data-driven discoveries in deep-time Earth (DE) include: substantial volumes of DE data are not digitized; many DE databases do not adhere to FAIR (findable, accessible, interoperable and reusable) principles; we lack a systematic knowledge graph for DE; existing DE databases are geographically heterogeneous; a significant fraction of DE data is not in open-access formats; tailored tools are needed. These challenges motivate the Deep-Time Digital Earth (DDE) program initiated by the International Union of Geological Sciences and developed in cooperation with national geological surveys, professional associations, academic institutions and scientists around the world. DDE’s mission is to build on previous research to develop a systematic DE knowledge graph, a FAIR data infrastructure that links existing databases and makes dark data visible, and tailored tools for DE data, which are universally accessible. DDE aims to harmonize DE data, share global geoscience knowledge and facilitate data-driven discovery in the understanding of Earth's evolution.
Collapse
Affiliation(s)
- Chengshan Wang
- State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Beijing 100083, China
| | - Robert M Hazen
- Earth and Planets Laboratory, Carnegie Institution for Science, Washington, DC 20015, USA
| | - Qiuming Cheng
- State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Beijing 100083, China
| | | | - Chenghu Zhou
- State Key Laboratory of Resources and Environment Information System, Institute of Geographical Science and Natural Resources, Chinese Academy of Sciences, Beijing 100101, China
| | - Peter Fox
- Tetherless World Constellation, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Shu-Zhong Shen
- School of Earth Sciences and Engineering, Nanjing University, Nanjing 210023, China
| | - Roland Oberhänsli
- Institute of Earth and Environmental Sciences, University of Potsdam, Potsdam 14476, Germany
| | - Zengqian Hou
- Institute of Geology, Chinese Academy of Geological Sciences, Beijing 100037, China
| | - Xiaogang Ma
- Department of Computer Science, University of Idaho, Moscow, ID 83844, USA
| | - Zhiqiang Feng
- Petroleum Exploration and Production Research Institute, SINOPEC, Beijing 100083, China
| | - Junxuan Fan
- School of Earth Sciences and Engineering, Nanjing University, Nanjing 210023, China
| | - Chao Ma
- Department of Computer Science, University of Idaho, Moscow, ID 83844, USA
| | - Xiumian Hu
- School of Earth Sciences and Engineering, Nanjing University, Nanjing 210023, China
| | - Bin Luo
- State Key Laboratory of Resources and Environment Information System, Institute of Geographical Science and Natural Resources, Chinese Academy of Sciences, Beijing 100101, China
| | - Juanle Wang
- State Key Laboratory of Resources and Environment Information System, Institute of Geographical Science and Natural Resources, Chinese Academy of Sciences, Beijing 100101, China
| | | |
Collapse
|