Towards Rapid and Accurate Seismic Event Detection and Localization Using DAS Data: Exploring the Source-Scanning Algorithm Method.

The Source Scanning Algorithm (SSA) was introduced (Kao and Shan, 2004; 2007) as an automated approach to detect and locate seismic events without the need for phase picking. This algorithm works by stacking observed waveforms based on theoretical arrival times derived from a velocity model, under the assumption of a potential point source. It conducts a grid search across multiple candidate source locations, identifying those that produce the highest stacking values (i.e., brightness) as the most probable hypocenters. Extensive use of this approach and its subsequent variations to continuous data streams has shown success even under challenging station geometries, noisy data, and complex seismic source conditions. Recently, Distributed Acoustic Sensing (DAS) has emerged as a powerful tool for seismic monitoring, employing standard fiber‐optic cables as dense arrays of virtual sensors. By measuring strain rate at meter‐scale intervals, DAS can offer continuous seismic coverage over large distances, including remote or hard‐to‐reach areas such as offshore regions, where deploying conventional seismometers can be impractical. With suitable processing, these strain rate measurements can be converted into particle motion, allowing the application of standard seismological methods. However, cable geometry and orientation may introduce azimuthal ambiguities, complicating the use of these methods. In this study, we evaluate the performance of the SSA for locating seismic events using exclusively DAS data. By analyzing both synthetic and real offshore datasets from diverse global regions (e.g., Chile, Greece), we systematically assess the effectiveness of SSA applied to continuous DAS measurements in accurately determining the locations of seismic events. This investigation raises new questions regarding the computational challenges involved—particularly the large volume of DAS data, the selection of appropriate characteristic functions, the integration of DAS data with local seismic networks, the velocity models used for travel-time calculations, and the necessary time corrections. Our results show that SSA can quickly and consistently detect seismic occurrences in DAS data without explicit phase picking, thereby offering a viable method for continuous monitoring even in the presence of the complicated geometries related with DAS installations. This study demonstrates the feasibility of combining DAS and SSA for high-resolution earthquake detection and highlights the potential for expanding its use in real-time seismic networks worldwide.

This research work was supported by the "SUBMarine cablEs for ReSearch and Exploration - SUBMERSE" EU-funded project (HORIZON-INFRA-2022-TECH-01, Grant Agreement No. 101095055).