Towards the Clustering of Large Distributed Acoustic Sensing Datasets

Distributed Acoustic Sensing (DAS) measures strain or strain rate along an optical fiber with a high spatial and temporal resolution. The typical channel distance is in the order of a few meters while the sampling frequency can reach 1 kHz or higher, which makes it possible to record a wide range of seismic signals.

The optical fibers used for DAS can be several kilometers long and measurements take place over days, weeks or months, resulting in very large datasets of up to several terabytes per day. However, due to this large amount of data, it is challenging to get a good impression of the different types of seismic signals present in the data, since a manual inspection can become immensely time-consuming.

In this study we aim to automatize this process by clustering the data to detect and categorize different types of seismic signals. A 2D continuous wavelet transform (CWT) is used to automatically extract features from the data. In contrast to many other approaches, this allows to not only use temporal information, but to also include the spatial dimension to further distinguish between different seismic sources and wave types.

The clustering is performed in two steps. First, a Gaussian Mixture Model (GMM) is used to cluster the features. Then, the final clusters are obtained by merging similar components of the GMM.

The application of the proposed procedure to different large DAS datasets provides valuable results. Identified clusters show different spatial and temporal patterns and correspond to seismic signals originating from various sources, such as car traffic, tramways or machinery.