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- S12A-01: Seismologically Lossless Compression of Distributed Acoustic Sensing Data via Compressive Sensing: Taiwan MiDAS Case Study
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NOLA CC
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Yang Ma, University of California Los Angeles (First Author, Presenting Author)
Lingsen Meng, University of California Los Angeles
Yen-Yu Lin, National Central University
Distributed Acoustic Sensing (DAS) is a technology that turns regular fiber-optic cables into sensors for detecting ground movement, creating a dense seismic array. This technology provides a huge amount of data that can be used to monitor earthquakes, but processing such big data volumes quickly and efficiently is a big challenge. In our study, we introduced a new method based on compressive sensing to compress and analyze DAS data. This method can reduce the data size by at least 15 times without losing important information. This method works well with common earthquake detection tools and allows real-time processing of compressed data. This research shows that compressive sensing can help manage and use DAS data more effectively, promoting its further development in the field of seismology.
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