Distributed Acoustic Sensing Data Compression for Seismological Applications via Compressive Sensing

Summary Distributed Acoustic Sensing (DAS) is an emerging technology that turns existing optical-fiber cables into high-density seismic arrays, generating vast amounts of observational data in various contexts. Consequently, large-scale storage, transmission and processing of DAS data present both challenges and opportunities in seismology. In this study, we propose a data compression and seismic detection workflow based on compressive sensing (CS) and apply it to DAS data from the Taiwan Milun Fault Drilling and All-inclusive Sensing (MiDAS) project. Our algorithm achieves a compression ratio of at least 15, with reconstructed data from specific earthquakes compatible with standard seismological algorithms. Additionally, we migrate the detection algorithm to the compressed domain, enabling quasi-real-time, high-accuracy seismic detection. This approach demonstrates the feasibility of directly processing compressed data, reducing computational burdens in DAS processing. Furthermore, we discuss an empirical criterion for determining the maximum compression ratio of a given signal based on CS. Our workflow is compared with other mature compression algorithms across eight different datasets to demonstrate its advantages, limitations, and applicability. Generally, the CS algorithm is more effective for high-SNR event-oriented DAS applications rather than continuous noise-dominated monitoring. Collectively, these results highlight the potential of the CS algorithm in advancing the development of efficient, user-friendly DAS data products.