  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. In this work, 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 20 times more earthquakes than previously
cataloged by the Oklahoma Geological Survey. Our algorithm is orders of
magnitude faster than established methods.
