Artificial intelligence for geoscience: Progress, challenges, and perspectives

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…

• NN
  National Natural Science Foundation of China

  Awards: 42022054, 62372470, 42325107, 41925007, 42090014, T2225019, 52121006, U21A2013, 42201415, 42077156, 42241109
• CA
  Chinese Academy of Sciences

  Awards: XDA23090303, EP/W034034/1
• YI
  Youth Innovation Promotion Association of the Chinese Academy of Sciences

  Award: 2023112
• EA
  Engineering and Physical Sciences Research Council

  Awards: EP/W034034/1, EP/W033984/1
• NK
  National Key Research and Development Program of China

  Award: 2022YFF0 500
• YI
  Youth Innovation Promotion Association