Convolutional neural network for earthquake detection and location

Science Advances
Thibaut PerolMarine Denolle

Abstract

The recent evolution of induced seismicity in Central United States calls for exhaustive catalogs to improve seismic hazard assessment. Over the last decades, the volume of seismic data has increased exponentially, creating a need for efficient algorithms to reliably detect and locate earthquakes. Today's most elaborate methods scan through the plethora of continuous seismic records, searching for repeating seismic signals. We leverage the recent advances in artificial intelligence and present ConvNetQuake, a highly scalable convolutional neural network for earthquake detection and location from a single waveform. We apply our technique to study the induced seismicity in Oklahoma, USA. We detect more than 17 times more earthquakes than previously cataloged by the Oklahoma Geological Survey. Our algorithm is orders of magnitude faster than established methods.

References

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Nov 26, 2015·Science Advances·F Rall Walsh, Mark D Zoback
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Dec 16, 2016·Science Translational Medicine·Léon C L van KempenAlan Spatz

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Citations

May 12, 2018·Science·Thibault CandelaLoes Buijze
Nov 7, 2019·Plants·Muhammad Hammad SaleemKhalid Mahmood Arif
Oct 20, 2019·Scientific Reports·Michael J BiancoFan-Chi Lin
May 29, 2018·Science Advances·Benjamin K HoltzmanDouglas Repetto
Jul 18, 2019·Scientific Reports·S Mostafa MousaviGregory C Beroza
Jul 31, 2019·Sensors·Byung-Kil HanSeung-Chan Kim
Jul 17, 2020·Optics Express·Chen ZhuJie Huang
Mar 23, 2019·Science·Karianne J BergenGregory C Beroza
Nov 24, 2020·PloS One·Benjamin D StryckerMarlan O Scully
Oct 13, 2020·Physics Reports·Jingfang FanHans Joachim Schellnhuber
Mar 6, 2021·Nature Communications·Wenhuan KuangJie Zhang
Mar 20, 2021·The Science of the Total Environment·Pan XiongHuiyu Zhou

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Methods Mentioned

BETA
feature extraction

Software Mentioned

EQcorrscan
FAST
ConvNetQuake
ADAM
TensorFlow

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