地球物理学的深度学习:当前和未来趋势

IF 25.2 1区 地球科学 Q1 GEOCHEMISTRY & GEOPHYSICS Reviews of Geophysics Pub Date : 2021-06-03 DOI:10.1029/2021RG000742
Siwei Yu, Jianwei Ma
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引用次数: 113

摘要

近年来,深度学习作为一种新的数据驱动技术,越来越受到地球物理学界的关注,同时也带来了许多机遇和挑战。深度学习被证明具有准确预测复杂系统状态的潜力,并缓解了大时空地球物理应用中的“维度诅咒”。我们通过回顾各种地球科学场景下的深度学习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重点。更多的应用,包括地球结构,水资源,大气科学和空间科学也进行了综述。此外,还讨论了在地球物理领域应用DL的困难。分析了近年来地球物理中深度学习的发展趋势。提出了未来地球物理学中涉及深度学习的研究方向,如无监督学习、迁移学习、多模态深度学习、联邦学习、不确定性估计和主动学习。编码教程和快速探索DL提示的总结是为初学者和地球物理学感兴趣的读者提出的。
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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.

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来源期刊
Reviews of Geophysics
Reviews of Geophysics 地学-地球化学与地球物理
CiteScore
50.30
自引率
0.80%
发文量
28
审稿时长
12 months
期刊介绍: Geophysics Reviews (ROG) offers comprehensive overviews and syntheses of current research across various domains of the Earth and space sciences. Our goal is to present accessible and engaging reviews that cater to the diverse AGU community. While authorship is typically by invitation, we warmly encourage readers and potential authors to share their suggestions with our editors.
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