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Over the past decades, the Groningen Gas Field (GGF) has been increasinglyfaced by induced earthquakes resulting from gas production. The seismicmonitoring network at Groningen has been recently densified to improve theseismic network performance, resulting in increasing amounts of seismic data.Although traditional automated event detection techniques generally aresuccessful in detecting events from continuous data, its detection success ischallenged in cases of lower signal-to-noise ratios. The data stream comingfrom these networks has initiated specific interest in neural networks forautomated classification and interpretation. Here, we explore the feasibilityof neural networks in detecting the occurrence of seismic events. For thispurpose, a three-layered feedforward neural network was trained using publicdata of a seismic event in the GGF obtained from the Royal NetherlandsMeteorological Institute (KNMI) data portal. The first arrival times andduration of earthquake waveforms determined by KNMI for a subset of the stationdata, were used to detect the arrival times and event duration for the otheruninterpreted station data. Subsequently various attributes were used as inputfor the neural network, that were based on different short term averaging/longterm averaging (STA/LTA) and frequency sub-band settings. Using these inputdata, the network's parameters were iteratively improved to maximize itscapability in successfully discriminating seismic events from noise anddetermine the event duration. Results show an increase of 65 % in accuratelydetecting seismic events and determining their duration as compared to thereference method. This clears the way for improved interpretation of signalwaveforms and automated seismic event classification in the Groningen area.

A Neural Network Approach for Improved Seismic Event Detection in the Groningen Gas Field, The Netherlands