DASPy: A Python Toolbox for DAS Seismology
Minzhe Hu, Zefeng Li
Abstract
Abstract Distributed acoustic sensing (DAS) has emerged as a novel technology in geophysics, owing to its high-sensing density, cost effectiveness, and adaptability to extreme environments. Nonetheless, DAS differs from traditional seismic acquisition technologies in many aspects: big data volume, equidistant sensing, measurement of axial strain (strain rate), and noise characteristics. These differences make DAS data processing challenging for new hands. To lower the bar of DAS data processing, we develop an open-source Python toolbox called DASPy, which encompasses classic seismic data processing techniques, including preprocessing, filter, spectrum analysis, and visualization, and specialized algorithms for DAS applications, including denoising, waveform decomposition, channel attribute analysis, and strain–velocity conversion. Using openly available DAS data as examples, this article makes an overview and tutorial on the eight modules in DASPy to illustrate the algorithms and practical applications. We anticipate DASPy to provide convenience for researchers unfamiliar with DAS data and help facilitate the rapid growth of DAS seismology.