Seismologically Lossless Compression of Distributed Acoustic Sensing Data via Compressive Sensing: Taiwan MiDAS Case Study
Yang Ma, Lingsen Meng, & Yen-Yu LinSubmitted September 7, 2025, SCEC Contribution #14362, 2025 SCEC Annual Meeting Poster #TBD
Distributed Acoustic Sensing (DAS) is an emerging technology that turns existing fiber-optic cables into high-density seismic arrays, generating vast amounts of observational data in various contexts. Consequently, large-scale transmission and processing of DAS data present both challenges and opportunities in seismology. This study proposes a data compression and seismic detection workflow based on Compressive Sensing (CS), applied to DAS data from the Taiwan Milun Fault Drilling and All-inclusive Sensing (MiDAS) project. Our algorithm achieves at least a 15-fold compression ratio, with reconstructed data from specific earthquakes fully compatible with classical seismological algorithms, such as ML-based phase picking and array seismology. Additionally, we migrated the classical STA/LTA detection algorithm to the compressed domain, enabling real-time, high-accuracy seismic detection.This workflow demonstrates the feasibility and possibility of directly processing compressed data, reducing computational burdens in DAS processing. Furthermore, we discussed the empirical criterion for determining the compression ratio of a specific signal based on CS. Our workflow is compared with other mature compress algorithms to demonstrate the advantages, limitations, and scope of applicability. Collectively, these results highlight the potential of the Compressive Sensing (CS) algorithm in advancing the development of efficient, user-friendly DAS data products.
Citation
Ma, Y., Meng, L., & Lin, Y. (2025, 09). Seismologically Lossless Compression of Distributed Acoustic Sensing Data via Compressive Sensing: Taiwan MiDAS Case Study. Poster Presentation at 2025 SCEC Annual Meeting.
Related Projects & Working Groups
Seismology
