Real-time and post-hoc compression for data from Distributed Acoustic Sensing. (September 2022)

• Record Type: Journal Article
• Authors: Dong, Bin; Popescu, Alex; Tribaldos, Verónica Rodríguez; Byna, Suren; Ajo-Franklin, Jonathan; Wu, Kesheng
• Is Part Of: Computers & geosciences. Volume 166(2022)
• Publication Date: 2022-09
• Subjects: Distributed Acoustic Sensing -- DAS -- Data compression -- Lossless -- Real-time -- Post-hoc -- Parallel -- HDF5 -- ZSTD -- TurboPFOR -- ZigZag; Environmental policy -- Periodicals; 550.5
• DOI: 10.1016/j.cageo.2022.105181
• Languages: English
• ISSNs: 0098-3004

Abstract

Abstract: Distributed Acoustic Sensing (DAS) is an emerging sensing technology that records the strain-rate along fiber optic cables at high spatial and temporal resolution. This technique is becoming a popular tool in seismology, hydrology, and other subsurface monitoring applications. However, due to the large coverage (10's of km) and high density of measurements (1m spacing at 100's of Hz), a DAS installation could produce terabytes of data records per day. Because many DAS instruments are deployed in remote locations, this large data size poses significant challenges to its transfer and storage. In this paper, we explore lossless compression methods to reduce the storage requirement in both real-time and post-hoc scenarios. We propose a two-stage compression method to improve the compression ratio and compression speed. This two-stage compression method could reduce the storage requirement by 40%, which is 20% more than other lossless methods, such as ZSTD. We demonstrate that the compression method could complete its operation well before the DAS instrument needs to output the next file, making it suitable for real-time DAS acquisition. We also implement a parallel compression method for a post-hoc scenario and demonstrate that our method could effectively utilize a parallel computer. With 256 CPU cores, our parallel compression method achieves the speed of 26GB/second. Highlights: We found that DAS datasets in short integer types are further compressable losslessly. We designed a lossless compression method to reduce the DAS data size by 40%. We developed an in-situ method to compress DAS data in real-time during acquisition. We developed a parallel method to compress DAS data during post-processing.