  Over the past decades, the Groningen Gas Field (GGF) has been increasingly
faced by induced earthquakes resulting from gas production. The seismic
monitoring network at Groningen has been recently densified to improve the
seismic network performance, resulting in increasing amounts of seismic data.
Although traditional automated event detection techniques generally are
successful in detecting events from continuous data, its detection success is
challenged in cases of lower signal-to-noise ratios. The data stream coming
from these networks has initiated specific interest in neural networks for
automated classification and interpretation. Here, we explore the feasibility
of neural networks in detecting the occurrence of seismic events. For this
purpose, a three-layered feedforward neural network was trained using public
data of a seismic event in the GGF obtained from the Royal Netherlands
Meteorological Institute (KNMI) data portal. The first arrival times and
duration of earthquake waveforms determined by KNMI for a subset of the station
data, were used to detect the arrival times and event duration for the other
uninterpreted station data. Subsequently various attributes were used as input
for the neural network, that were based on different short term averaging/long
term averaging (STA/LTA) and frequency sub-band settings. Using these input
data, the network's parameters were iteratively improved to maximize its
capability in successfully discriminating seismic events from noise and
determine the event duration. Results show an increase of 65 % in accurately
detecting seismic events and determining their duration as compared to the
reference method. This clears the way for improved interpretation of signal
waveforms and automated seismic event classification in the Groningen area.
