Effects of cable geometry and specific noise sources on DAS monitoring potential


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<p>The Distributed Acoustic Sensing (DAS) method re-purposes fiber optic cables into a very-dense array of strain/strain-rate sensors, <span class="Y2IQFc" lang="en">capable of detecting different types of seismic events. </span>However, DAS data are characterized by lower SNRs compared with standard seismic sensors, mainly because of a) strong directivity effects, 2) ground coupling inhomogeneities, and 3) site effects. Hence, beyond the array geometry, specific noise sources may reduce the potential of DAS for seismic monitoring. Previous research has already shown successful case-studies for event detection/location. Nevertheless, a coherent test on the performances of various arrays of different sizes and geometries is still lacking.</p> <p>In this study, an extensive DAS database is organized for such a goal, including 15 DAS arrays that recorded at least one seismic event (located at a range of distances from the arrays). P wave arrival times are exploited to estimate the epicentral parameters with a Markov Chain Monte Carlo method. Then, to analyze the effects of cable geometry and potential sources of noise/ambiguity on the location uncertainties, a series of synthetic tests are performed, where synthetic traveltimes are modified as follows: a) adding noise with equal variance to all the DAS channels (SYNTH-01), b) adding noise characterized by an increasing variance with the distance from the event (SYNTH-02), c) simulating the mis-pick between P and S phases (SYNTH-03) and d) adding noise with a variance influenced by cable coupling inhomogeneities (SYNTH-04). Results show that the epicentral locations with automatic P wave arrival times have different degrees of uncertainty, given the geometrical relation between the event and the DAS arrays. This behavior is confirmed by the SYNTH-01 test, indicating that specific geometries provide a lower constraint on event location. Moreover, SYNTH-04 shows that simulating cable coupling inhomogeneities primarily reproduces the observed location uncertainties. Finally, some cases are not explained by any of the synthetic tests, stressing the possible presence of more complex noise sources contaminating the signals.</p>
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