  Distributed Acoustic Sensing (DAS) is becoming increasingly popular in
microseismic monitoring operations. This data acquisition technology converts
fiber-optic cables into dense arrays of seismic sensors that can sample the
seismic wavefield produced by active or passive sources with a high spatial
density, over distances ranging from a few hundred meters to tens of
kilometers. However, standard microseismic data analysis procedures have
several limitations when dealing with the high spatial (inter-sensor spacing up
to sub-meter scale) sampling rates of DAS systems. Here we propose a
semblance-based seismic event detection method that fully exploits the high
spatial sampling of the DAS data. The detector identifies seismic events by
computing waveform coherence of the seismic wavefield along geometrical
hyperbolic trajectories for different curvatures and positions of the vertex,
which are completely independent from external information (i.e. velocity
models). The method detects a seismic event when the coherence values overcome
a given threshold and satisfies our clustering criteria. We first validate our
method on synthetic data and then apply it to real data from the FORGE
geothermal experiment in Utah, USA. Our method detects about two times the
number of events obtained with a standard method when applied to 24h of data.
