Zhao Tianjie, Wang Sheng, Ouyang Chaojun, Chen Min, Liu Chenying, Zhang Jin, Yu Long, Wang Fei, Xie Yong, Li Jun, Wang Fang, Grunwald Sabine, Wong Bryan M, Zhang Fan, Qian Zhen, Xu Yongjun, Yu Chengqing, Han Wei, Sun Tao, Shao Zezhi, Qian Tangwen, Chen Zhao, Zeng Jiangyuan, Zhang Huai, Letu Husi, Zhang Bing, Wang Li, Luo Lei, Shi Chong, Su Hongjun, Zhang Hongsheng, Yin Shuai, Huang Ni, Zhao Wei, Li Nan, Zheng Chaolei, Zhou Yang, Huang Changping, Feng Defeng, Xu Qingsong, Wu Yan, Hong Danfeng, Wang Zhenyu, Lin Yinyi, Zhang Tangtang, Kumar Prashant, Plaza Antonio, Chanussot Jocelyn, Zhang Jiabao, Shi Jiancheng, Wang Lizhe
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China.
School of Computer Science, China University of Geosciences, Wuhan 430078, China.
Innovation (Camb). 2024 Aug 22;5(5):100691. doi: 10.1016/j.xinn.2024.100691. eCollection 2024 Sep 9.
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.
本文探讨了地球科学探究的演变，追溯了从传统的基于物理的模型到由人工智能（AI）和数据收集技术的重大进步所推动的现代数据驱动方法的发展历程。传统模型基于物理和数值框架，通过明确重构潜在的物理过程提供有力的解释。然而，它们在全面捕捉地球的复杂性和不确定性方面的局限性，在优化和实际应用中带来了挑战。相比之下，当代数据驱动模型，特别是那些利用机器学习（ML）和深度学习（DL）的模型，利用大量地球科学数据来获取见解，而无需详尽的理论知识。ML技术在解决与地球科学相关的问题方面已显示出前景。尽管如此，诸如数据稀缺、计算需求、数据隐私问题以及AI模型的“黑箱”性质等挑战阻碍了它们无缝融入地球科学。将基于物理的方法和数据驱动的方法整合到混合模型中提供了一种替代范式。这些模型结合领域知识来指导AI方法，在减少训练数据需求的情况下展示出更高的效率和性能。本综述全面概述了地球科学研究范式，强调了先进AI技术与地球科学交叉领域中未被发掘的机会。它审视了主要方法，展示了大规模模型的进展，并讨论了将塑造地球科学中AI未来格局的挑战和前景。本文勾勒出一个充满可能性的动态领域，有望开启对地球复杂性的新理解，并进一步推进地球科学探索。
