Mousavi S Mostafa, Beroza Gregory C
Department of Geophysics, Stanford University, Stanford, CA 94305, USA.
Google, Mountain View, CA 94043, USA.
Science. 2022 Aug 12;377(6607):eabm4470. doi: 10.1126/science.abm4470.
Seismic waves from earthquakes and other sources are used to infer the structure and properties of Earth's interior. The availability of large-scale seismic datasets and the suitability of deep-learning techniques for seismic data processing have pushed deep learning to the forefront of fundamental, long-standing research investigations in seismology. However, some aspects of applying deep learning to seismology are likely to prove instructive for the geosciences, and perhaps other research areas more broadly. Deep learning is a powerful approach, but there are subtleties and nuances in its application. We present a systematic overview of trends, challenges, and opportunities in applications of deep-learning methods in seismology.
来自地震和其他来源的地震波被用于推断地球内部的结构和特性。大规模地震数据集的可用性以及深度学习技术对地震数据处理的适用性，已将深度学习推到了地震学中基础的、长期研究调查的前沿。然而，将深度学习应用于地震学的某些方面可能会对地球科学乃至更广泛的其他研究领域具有指导意义。深度学习是一种强大的方法，但其应用存在一些微妙之处和细微差别。我们对深度学习方法在地震学中的应用趋势、挑战和机遇进行了系统概述。
