Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2021GL093157 |
Identification of low-frequency earthquakes on the San Andreas fault with deep learning | |
A. M. Thomas; A. Inbal; J. Searcy; D. R. Shelly; R. Bü; rgmann | |
2021-06-26 | |
发表期刊 | Geophysical Research Letters
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出版年 | 2021 |
英文摘要 | Low-frequency earthquakes are a seismic manifestation of slow fault slip. Their emergent onsets, low amplitudes, and unique frequency characteristics make these events difficult to detect in continuous seismic data. Here we train a convolutional neural network to detect low-frequency earthquakes near Parkfield, CA using the catalog of Shelly (2017) as training data. We explore how varying model size and targets influence the performance of the resulting network. Our preferred network has a peak accuracy of 85% and can reliably pick LFE S-wave arrival times on single station records. We demonstrate the abilities of the network using data from permanent and temporary stations near Parkfield, and show that it detects new LFEs that are not part of the Shelly (2017) catalog. Overall, machine learning approaches show great promise for identifying additional low-frequency earthquake sources. The technique is fast, generalizable, and does not require sources to repeat. |
领域 | 气候变化 |
URL | 查看原文 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/333652 |
专题 | 气候变化 |
推荐引用方式 GB/T 7714 | A. M. Thomas,A. Inbal,J. Searcy,et al. Identification of low-frequency earthquakes on the San Andreas fault with deep learning[J]. Geophysical Research Letters,2021. |
APA | A. M. Thomas,A. Inbal,J. Searcy,D. R. Shelly,R. Bü,&rgmann.(2021).Identification of low-frequency earthquakes on the San Andreas fault with deep learning.Geophysical Research Letters. |
MLA | A. M. Thomas,et al."Identification of low-frequency earthquakes on the San Andreas fault with deep learning".Geophysical Research Letters (2021). |
条目包含的文件 | 条目无相关文件。 |
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