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DOI10.1029/2019GL085976
A Machine-Learning Approach for Earthquake Magnitude Estimation
Mousavi S.M.; Beroza G.C.
发表日期2020
ISSN 0094-8276
卷号47期号:1
英文摘要In this study, we present a fast and reliable method for end-to-end estimation of earthquake magnitude from raw waveforms recorded at single stations. We design a regressor (MagNet) composed of convolutional and recurrent neural networks that is not sensitive to the data normalization, hence waveform amplitude information can be utilized during the training. The network can learn distance-dependent and site-dependent functions directly from the training data. Our model can predict local magnitudes with an average error close to zero and standard deviation of ~0.2 based on single-station waveforms without instrument response correction. We test the network for both local and duration magnitude scales and show a station-based learning can be an effective approach for improving the performance. The proposed approach has a variety of potential applications from routine earthquake monitoring to early warning systems. ©2019. American Geophysical Union. All Rights Reserved.
英文关键词Deep learning; Learning systems; Machine learning; Recurrent neural networks; Early Warning System; Earthquake magnitudes; Earthquake monitoring; Machine learning approaches; magnitude; Response corrections; single-station; Source characterization; Earthquakes; amplitude; artificial neural network; design method; earthquake magnitude; information management; seismometry; waveform analysis
语种英语
来源期刊Geophysical Research Letters
文献类型期刊论文
条目标识符http://gcip.llas.ac.cn/handle/2XKMVOVA/170895
作者单位Geophysics Department, Stanford University, Stanford, CA, United States
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Mousavi S.M.,Beroza G.C.. A Machine-Learning Approach for Earthquake Magnitude Estimation[J],2020,47(1).
APA Mousavi S.M.,&Beroza G.C..(2020).A Machine-Learning Approach for Earthquake Magnitude Estimation.Geophysical Research Letters,47(1).
MLA Mousavi S.M.,et al."A Machine-Learning Approach for Earthquake Magnitude Estimation".Geophysical Research Letters 47.1(2020).
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