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              <p>Distributed Acoustic Sensing (DAS) turns a fiber optic into a very dense network of equally-distributed seismic sensors. We focused on the high-density sampling of the seismic wavefield, expressed in strain rates, measured by DAS. Classical approaches used to identify seismic signals rely on the recorded features at one station, but it is difficult to include spatial information in case of dense seismic station networks. This work aims at introducing new spatial and similarity features for seismic event classification suitable to analyze DAS observations. We propose a processing chain based on the XGBoost algorithm and the use of specifically designed spatio-temporal and similarity features for the event classification, and Markov Random Field for the spatial clustering. The methodology is designated to be applied on a continuous stream of DAS observations. We tested our processing chain to detect earthquakes and quarry blasts recorded in the region by permanent seismic networks and included in the RENASS catalog. These events are part of a strain-rate seismic survey carried out during a 3 weeks campaign of DAS measurements along Ã 91 km fiber optic cable deployed in the central Pyrenees mountains (France). Despite the high anthropogenic activities along the fiber optic path, the proposed method succeeded in detecting earthquakes of magnitude &amp;gt;0.4 and quarry blasts of magnitude &amp;gt;1.0 while limiting the number of false alarms. This performance is particularly noteworthy for low-magnitude events, where detection is accomplished despite a lower signal-to-noise ratio compared to traditional seismometers. The methodology opens the door to real time detection and classification of seismic events measured with long-distance fiber optic systems.</p>
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