Deep-learning seismology

被引:11
作者
Mousavi, S. Mostafa [1 ,2 ]
Beroza, Gregory C. [1 ]
机构
[1] Stanford Univ, Dept Geophys, Stanford, CA 94305 USA
[2] Google, Mountain View, CA 94043 USA
关键词
CONVOLUTIONAL NEURAL-NETWORK; WAVE-FORM INVERSION; SEISMIC FACIES ANALYSIS; PHYSICS; MODEL; EARTHQUAKES; FRAMEWORK; PICKING; NOISE; CLASSIFICATION;
D O I
10.1126/science.ahm4470
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页码:725 / +
页数:12
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