DASView: A PyQt-Based Visualization and Processing Tool for Distributed Acoustic Sensing Data

Abstract Distributed acoustic sensing (DAS) has emerged as a transformative geophysical sensing technology, with growing applications in seismic monitoring, intelligent traffic surveillance, and urban subsurface imaging, driven by its high spatial density, cost efficiency, and environmental robustness. However, DAS data are typically massive and ultradensely sampled, which fundamentally differs from conventional seismic records and presents significant challenges for data visualization and signal processing. To lower the technical barriers for DAS data analysis, we develop an interactive visualization and processing tool based on the PyQt framework. The tool integrates both classical seismic data processing workflows, such as data slicing, filtering, and spectral analysis, as well as DAS-specific functionalities, including denoising and channel attribute analysis. Core features include 2D waterfall visualization, interactive single-channel waveform and spectrum inspection, and flexible figure export. In addition, the platform supports modules for travel-time extraction and automated traffic signal tracking. We also provide openly accessible example datasets to facilitate method validation. This tool is intended to support DAS researchers and practitioners by offering an accessible and efficient platform for DAS data exploration, processing, and visualization, thereby contributing to the broader adoption and advancement of DAS-based sensing technologies.