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.

中文翻译：

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地球物理学深度学习：当前和未来趋势

最近，深度学习（DL）作为一种与传统方法相比的新数据驱动技术，在地球物理界引起了越来越多的关注，带来了许多机遇和挑战。DL 已被证明具有准确预测复杂系统状态并缓解大型时空地球物理应用中的“维数灾难”的潜力。我们通过回顾各种地球科学场景中的 DL 方法来解决基本概念、最新文献和未来趋势。勘探地球物理、地震和遥感是主要的焦点。还审查了更多应用，包括地球结构、水资源、大气科学和空间科学。此外，还讨论了在地球物理界应用深度学习的困难。分析了近年来地球物理学中DL的发展趋势。为未来地球物理学中涉及 DL 的研究提供了几个有希望的方向，例如无监督学习、迁移学习、多模态 DL、联邦学习、不确定性估计和主动学习。为初学者和感兴趣的地球物理学读者提供了编码教程和快速探索 DL 的技巧摘要。