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ABSTRACT Detecting offshore earthquakes in real time is challenging for traditional land-based seismic networks due to insufficient station coverage. Application of distributed acoustic sensing (DAS) to submarine cables has the potential to extend the reach of seismic networks and thereby improve real-time earthquake detection and earthquake early warning (EEW). We present a complete workflow of a modified point-source EEW algorithm, which includes a machine-learning-based model for P- and S-wave phase picking, a grid-search location method, and a locally calibrated empirical magnitude estimation equation. Examples are shown with offshore earthquakes from the SeaFOAM DAS project using a 52-km-long submarine cable in Monterey Bay, California, demonstrating the robustness of the proposed workflow. When comparing to the current onshore network, we can expect up to 6 s additional warning time for earthquakes in the offshore San Gregorio fault zone, representing a substantial improvement to the existing ShakeAlert EEW system. 

Abstract Distributed acoustic sensing (DAS) is being explored in a variety of environments as a promising technology for the recording of seismic signals in dense array configurations. There is a particular interest for deploying DAS arrays on the ocean floor, presenting formidable challenges for conventional seismology. Taking advantage of the availability of a dark fiber on the Monterey Bay Accelerated Research System (MARS) 52 km offshore cable at Monterey Bay, California, in July 2022, we installed a DAS interrogator at the shore end of the cable with the intention of acquiring continuous data for a period of one year. Here, we describe the experiment and present examples of observations over the first six months of the deployment.