Integrating Submarine DAS into a Regional Seismic Network for Enhanced Offshore Earthquake Location: A Case Study from the Canary Islands

Abstract Repurposing a submarine telecommunication cable as a distributed acoustic sensor (DAS) significantly enhances earthquake monitoring in the Canary Archipelago, where much of the seismicity occurs offshore. We interrogated two ∼60 km segments of a dark-fiber cable between Tenerife and Gran Canaria from 26 July to 29 September 2020. P and S waves of local earthquakes were first detected using a deep learning model for phase picking and then merged with arrival-time readings from the Instituto Geográfico Nacional seismic network. To prevent overweighting the dense DAS array relative to the sparser land-based stations, we designed a selection strategy that uses a machine-learning-based channel quality index algorithm to automatically retain a limited number of high-signal-to-noise DAS channels per event. The selected channels are spaced according to the actual interstation distances of the National Seismic Network on the islands. We relocated 31 earthquakes (1.1≤mbLg≤3.2) using a nonlinear, probabilistic earthquake location method. The addition of DAS data narrowed the median azimuthal gap by ∼12° and reduced the median depth uncertainty from over 12 to 3.4 km. Within the Enmedio submarine volcano corridor, the hybrid DAS-land network detected 66% of events with mbLg≥1.5, and all events above mbLg=2.0 within 50 km of the cable. These results demonstrate that even a modest number of well-selected DAS channels can significantly improve the precision of earthquake location in submarine environments, providing a scalable and operationally compatible path for enhancing offshore seismic and volcanic monitoring. The reduced volume of data also ensures that DAS integration does not overwhelm existing processing pipelines, making it feasible for use in real-time alert systems.