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Seismic Phase Picking Using Convolutional Networks
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DOI：10.1109/TGRS.2019.2911402.png)
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摘要
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When a seismometer network records an earthquake, operators will manually review the waveforms and identify the wave phases, a task known as phase picking. Manual phase picking is a time-consuming process that can be automated using machine learning; however, automatic methods have not yet achieved human-level performance, and open-source implementations of state-of-the-art algorithms are not always available. Convolutional networks have revolutionized the field of image processing, where the large amounts of readily available data make possible near-human performance in tasks such as classification and segmentation. Fortunately, phase picking is also an area where thousands of phases are manually picked, which makes convolutional networks a good fit for the processing of this type of data. In this paper, we describe Cospy, an open-source convolutional phase picker that uses a two-stage analysis in which the first stage segments a rough area around the phase, and the second stage regresses the precise location. Our approach was evaluated on the Northern California Earthquake Data Center (NCEDC) data set and, when targeting picks closer than 0.1 s, it achieved an F-1-score of 93.13% for P phases and 91.07% for S phases. Our results show that convolutional networks are on track to achieve human-level performance on the task of seismic phase picking and can contribute to decreasing the need for manual analysis.
Keyword:
Convolutional networks
Hough voting
phase picking
semantic segmentation
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8.6
论文数:
2.1W
被引数:
10.7W
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分享收藏Convolutional neural network for earthquake detection and location用于地震检测与定位的卷积神经网络.png)
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