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Sci Adv. 2018 Feb 14;4(2):e1700578. doi: 10.1126/sciadv.1700578. eCollection 2018 Feb.

Convolutional neural network for earthquake detection and location.

Author information

1
John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA.
2
Gram Labs Inc., Arlington, VA 22201, USA.
3
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
4
Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA 02138, USA.

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.

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