Artificial intelligence for geoscience:Progress,challenges,and perspectives  

作　　者：Tianjie Zhao Sheng Wang Chaojun Ouyang Min Chen Chenying Liu Jin Zhang Long Yu Fei Wang Yong Xie Jun Li Fang Wang Sabine Grunwald Bryan MWong Fan Zhang Zhen Qian Yongjun Xu Chengqing Yu Wei Han Tao Sun Zezhi Shao Tangwen Qian Zhao Chen Jiangyuan Zeng Huai Zhang Husi Letu Bing Zhang Li Wang Lei Luo Chong Shi Hongjun Su Hongsheng Zhang Shuai Yin Ni Huang Wei Zhao Nan Li Chaolei Zheng Yang Zhou Changping Huang Defeng Feng Qingsong Xu Yan Wu Danfeng Hong Zhenyu Wang Yinyi Lin Tangtang Zhang Prashant Kumar Antonio Plaza Jocelyn Chanussot Jiabao Zhang Jiancheng Shi Lizhe Wang 

机构地区：[1]Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100094,China [2]School of Computer Science,China University of Geosciences,Wuhan 430078,China [3]State Key Laboratory of Mountain Hazards and Engineering Resilience,Institute of Mountain Hazards and Environment,Chinese Academy of Sciences,Chengdu 610299,China [4]Key Laboratory of Virtual Geographic Environment(Ministry of Education of PRC),Nanjing Normal University,Nanjing 210023,China [5]Data Science in Earth Observation,Technical University of Munich,80333 Munich,Germany [6]The National Key Laboratory of Water Disaster Prevention,Yangtze Institute for Conservation and Development,Hohai University,Nanjing 210098,China [7]Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,China [8]School of Geographical Sciences,Nanjing University of Information Science and Technology,Nanjing 210044,China [9]State Key Laboratory of Soil and Sustainable Agriculture,Institute of Soil Science,Chinese Academy of Sciences,Nanjing 210008,China [10]Soil,Water and Ecosystem Sciences Department,University of Florida,PO Box 110290,Gainesville,FL,USA [11]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 [12]Institute of Remote Sensing and Geographical Information System,School of Earth and Space Sciences,Peking University,Beijing 100871,China [13]Key Laboratory of Computational Geodynamics,University of Chinese Academy of Sciences,Beijing 100049,China [14]International Research Center of Big Data for Sustainable Development Goals,Beijing 100094,China [15]College of Geography and Remote Sensing,Hohai University,Nanjing 211100,China [16]Department of Geography,The University of Hong Kong,Hong Kong 999077,SAR,China [17]Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control,Nanjing 210044,China [18]School of Environmental Science and Engineering,Nanjing University of Informa

出　　处：《The Innovation》2024年第5期136-160,135,共26页创新（英文）

基　　金：supported by National Natural Science Foundation of China(T2225019,41925007,62372470,U21A2013,42201415,42022054,42241109,42077156,52121006,42090014,and 42325107);the National Key R&D Programme of China(2022YFF0500);the Youth Innovation Promotion Association CAS(2023112);the Strategic Priority Research Program of CAS(XDA23090303);the RECLAIM Network Plus(EP/W034034/1).

摘　　要：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.

关 键 词：EARTH utilizing LANDSCAPE 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]