Integrating DAS into Seismic Monitoring Systems: Insights from SED and the BedrettoLab

We present our efforts to integrate Distributed Acoustic Sensing (DAS) data into the real-time seismic monitoring workflows of the Swiss Seismological Service (SED) and the Bedretto Underground Laboratory for Geosciences and Geoenergies (BedrettoLab). Spanning from regional national monitoring to fluid injection experiments at the BedrettoLab, we show how DAS can be used effectively across many orders of magnitude in temporal, spatial and amplitude resolution. Using temporary DAS deployments across Switzerland, we have incorporated DAS data into SeisComP, the existing monitoring infrastructure, to improve the accuracy and efficiency of seismic analyses. Our workflow involves spatial and temporal decimation, converting native DAS data into strain and velocity timeseries, and preparing it for seamless integration with traditional seismic data using MiniSEED and FDSN StationXML metadata. We will share examples of manual earthquake analyses, demonstrating how DAS data complements traditional seismic datasets for picking, locating, and magnitude estimation. These examples highlight how DAS can enhance event detection and characterization. In real-time, we demonstrate how DAS is combined with traditional seismic data for automated monitoring. The examples also include a case study using a 42-meter borehole section at the BedrettoLab during hydraulic stimulation for the FEAR project:  a single-mode loose fibre-optic cable was interrogated and sampled at 80 cm intervals with gauge length of 4 m and 4000 samples per second, enhancing the real-time monitoring of induced microseismicity. In addition, we are investigating the instrumental noise levels in DAS strain-rate data and studying earthquake amplitude decay models to better understand DAS performance in different seismic scenarios across different scales. We discuss the challenges faced during DAS integration, the lessons learned, and future directions, including improving DAS-based workflows for real-time monitoring and exploring its potential for early earthquake warning systems.