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Abstract

Recent work has shown increasing utility of Distributed Acoustic Sensing (DAS) in microseismic monitoring operations, including those related to Enhanced Geothermal Systems. DAS can transform km-long fiber optic cables into a distributed array of seismic sensors capable of imaging the entire wavefield. However, the large quantity of data DAS acquisition produces hurdles to near-real-time seismological data analysis. In this study, we test waveform-coherence-based methods used to detect and locate microseismic events with DAS data that can be applied in near-real time. The merits of the approach rest in its potential to enable continuous seismic monitoring using DAS. The detection approach uses a semblance-based method that evaluates waveform coherence along hypoerbolic trajectories with different curvatures and vertex positions. It returns a time series of coherence values that enable the declaration a seismic event when coherence values exceed a given threshold. We then test a waveform-stacking method based on the use of trace short-term-average/long-term-average (STA/LTA) values to estimate event hypocenter locations. We stack waveforms based on theoretical travel times for both P- and S-phases and constrain the source location based on peak stacked amplitude values. A grid search of variable source locations and origin times produces the best-fit solution where the stacked amplitudes reach peak values. The method does not require phase identification and picking and can be applied to low signal-to-noise-ratio data. We will present results of the method using synthetic data and validation on real data from the 2022 FORGE (Utah, USA) DAS dataset.