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Distributed Acoustic Sensing (DAS) is becoming increasingly popular inmicroseismic monitoring operations. This data acquisition technology convertsfiber-optic cables into dense arrays of seismic sensors that can sample theseismic wavefield produced by active or passive sources with a high spatialdensity, over distances ranging from a few hundred meters to tens ofkilometers. However, standard microseismic data analysis procedures haveseveral limitations when dealing with the high spatial (inter-sensor spacing upto sub-meter scale) sampling rates of DAS systems. Here we propose asemblance-based seismic event detection method that fully exploits the highspatial sampling of the DAS data. The detector identifies seismic events bycomputing waveform coherence of the seismic wavefield along geometricalhyperbolic trajectories for different curvatures and positions of the vertex,which are completely independent from external information (i.e. velocitymodels). The method detects a seismic event when the coherence values overcomea given threshold and satisfies our clustering criteria. We first validate ourmethod on synthetic data and then apply it to real data from the FORGEgeothermal experiment in Utah, USA. Our method detects about two times thenumber of events obtained with a standard method when applied to 24h of data.

A Semblance-based Microseismic Event Detector for DAS Data