RT Journal Article
SR Electronic
T1 Convolutional neural network for earthquake detection and location
JF Science Advances
JO Sci Adv
FD American Association for the Advancement of Science
SP e1700578
DO 10.1126/sciadv.1700578
VO 4
IS 2
A1 Perol, Thibaut
A1 Gharbi, Michaël
A1 Denolle, Marine
YR 2018
UL http://advances.sciencemag.org/content/4/2/e1700578.abstract
AB 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.