  The recent exploitation of natural resources and associated waste water
injection in the subsurface have induced many small and moderate earthquakes in
the tectonically quiet Central United States. This increase in seismic activity
has produced an exponential growth of seismic data recording, which brings the
necessity for efficient algorithms to reliably detect earthquakes among this
large amount of noisy data. Most current earthquake detection methods are
designed for moderate and large events and, consequently, they tend to miss
many of the low-magnitude earthquake that are masked by the seismic noise.
Perol et. al (2018) has focused on the problem of earthquake detection by using
a deep-learning approach: the authors proposed a convolutional neural network
(ConvNetQuake) to detect and locate earthquake events from seismic records.
This reports aims at reproducing part of the methodology proposed by the
author, which is the implementation of a convolutional neural network for
classification of events (i.e., earthquake vs. noise) from seismic records.
