Distributed Acoustic Sensing (DAS) is a recently established technology that has considerable potential for monitoring subsurface microseismic activity. Increasing numbers of wells have fibre optic cable installations and with a DAS interrogator these cables can be converted into high-density high-coverage seismic arrays. However, DAS systems can have higher noise floors than that of geophone arrays which make these dense datasets technically difficult to handle and a challenge to accurately pick P- and S-phase arrivals from. Traditional manual approaches are not viable on such sizeable data volumes, and hence automated solutions must be developed for efficient analysis. Here we test how conventional filtering and picking methods can be combined and applied to a DAS microseismic dataset. These approaches are tested on a single hydraulic fracture stage recorded by Silixa Ltd. iDAS system. 302 events were automatically detected and windowed. We manually pick the events to approximate a velocity model and then locate the events. We then test filtering (median, Wiener, bandpass, F-K) and picking methodologies including standard STA/LTA and a guided STA/LTA. The combined approaches that we have developed have produced equivalent results to manual picking but are significantly faster while analysing much higher density of traces.
