Deep Learning for Geophysics: Current and Future Trends

TLDR

Deep learning, a data‑driven technique, has attracted growing attention in geophysics for its ability to accurately predict complex system states and mitigate the curse of dimensionality. This review surveys current DL approaches in geosciences and proposes future research directions such as unsupervised learning, transfer learning, multimodal DL, federated learning, uncertainty estimation, and active learning. The authors examine DL applications across exploration geophysics, earthquakes, remote sensing, Earth structure, water resources, atmospheric and space science, while discussing challenges and recent trend analyses. A coding tutorial and concise tips for rapidly exploring DL are provided to aid beginners and interested geophysics researchers.

Abstract

Abstract Recently deep learning (DL), as a new data‐driven technique compared to conventional approaches, has attracted increasing attention in geophysical community, resulting in many opportunities and challenges. DL was proven to have the potential to predict complex system states accurately and relieve the “curse of dimensionality” in large temporal and spatial geophysical applications. We address the basic concepts, state‐of‐the‐art literature, and future trends by reviewing DL approaches in various geosciences scenarios. Exploration geophysics, earthquakes, and remote sensing are the main focuses. More applications, including Earth structure, water resources, atmospheric science, and space science, are also reviewed. Additionally, the difficulties of applying DL in the geophysical community are discussed. The trends of DL in geophysics in recent years are analyzed. Several promising directions are provided for future research involving DL in geophysics, such as unsupervised learning, transfer learning, multimodal DL, federated learning, uncertainty estimation, and active learning. A coding tutorial and a summary of tips for rapidly exploring DL are presented for beginners and interested readers of geophysics.