Deep Learning for Geophysics: Current and Future Trends

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. Plain Language Summary With the rapid development of artificial intelligence (AI), students and researchers in the geophysical community would like to know what AI can bring to geophysical discoveries. We present a review of deep learning (DL), a popular AI technique, for geophysical readers to understand recent advances, open problems, and future trends. This review aims to pave the way for more geophysical researchers, students, and teachers to understand and use DL techniques. Key Points The concept of deep learning (DL) and classical architectures of deep neural networks are introduced A review of state‐of‐the‐art DL methods in geophysical applications is provided The future directions for developing new DL methods in geophysics are discussed