Coherence-based methods for early data exploration of DAS data

Due to its dense spatial sampling of the wavefield, Distributed Acoustic Sensing (DAS) holds appeal for various applications in seismology. However, it produces large and complex datasets that are practically unfeasible to analyze manually. Additionally, we often lack prior knowledge of expected signals and their source distribution. This leads to the necessity of new processing tools, particularly for early data exploration. To address this challenge, we leverage signal coherence to detect and characterize seismic sources recorded by DAS. We segment the dataset into short time windows and compute the coherence matrix for each window at all frequencies, followed by averaging across different frequency bands of interest. Subsequently, we employ non-negative matrix factorization to isolate sources and retrieve their time-dependent coefficients. The resulting features display locally well-defined regions of elevated coherence. Different frequency bands can be analyzed simultaneously, which helps discriminate between signals originating from similar locations. Additionally, there is no need to make any assumptions about the data prior to applying the processing workflow. We apply this methodology to an urban DAS dataset, demonstrating its capability to detect coherent signals in complex settings. Notably, one feature reveals stable and repetitive sources of surface waves suitable for time-lapse monitoring of the subsurface. This makes our approach particularly interesting for applications of seismic interferometry, where understanding source distribution is crucial. We further demonstrate the value of coherence-based methods by applying them to a volcano dataset. Eigendecomposition of coherence matrices enables the detection and characterization of various volcanic signals such as explosions, earthquakes, and tremor pulses.