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Journal: |
Journal of Geophysical Research 2019 No.7
clicks:129 |
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Title:
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Author:
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Jian Wang1,2,3 , Zhuowei Xiao1,4, Chang Liu4 , Dapeng Zhao5 , and Zhenxing Yao1,2 |
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Adress: |
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Abstract:
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Arrival times of seismic phases contribute substantially to the study of the inner working of
the Earth. Despite great advances in seismic data collection, the usage of seismic arrival times is still
insufficient because of the overload manual picking tasks for human experts. In this work we employ a
deep‐learning method (PickNet) to automatically pick much more P and S wave arrival times of local
earthquakes with a picking accuracy close to that by human experts, which can be used directly to
determine seismic tomography. A large number of high‐quality seismic arrival times obtained with the
deep‐learning model may contribute greatly to improve our understanding of the Earth's
interior structure.
Plain Language Summary Deep learning is currently attracting immense research interest in
seismology due to its powerful ability to deal with huge seismic data collections. In this study we
developed a deep‐learning model (PickNet) that can rapidly pick a great number of first P and S wave arrival
times precisely from local earthquake seismograms. The picking accuracy of the arrival times provided by
our PickNet model is close to that by human experts. The data are good enough to be used directly to
determine high‐resolution 3‐D velocity models of the Earth. Our PickNet model can deal with seismic
waveforms provided by data centers of different earthquake networks. Furthermore, our PickNet model is
also a potential tool for automatically picking later seismic phases accurately. A large number of high‐quality
seismic arrival times can be used to illuminate the Earth structure clearly. Hence, this study may greatly
contribute to improve our knowledge of the Earth's interior. |
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