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Abstract

Given the scarcity of seismometers in marine environments, traditional seismology has limited effectiveness in oceanic regions. Submarine Distributed Acoustic Sensing (DAS) systems offer a promising alternative for seismic monitoring in these areas. However, the existing machine learning model trained on land-based DAS data does not perform well with submarine DAS due to differences in noise characteristics, deployment conditions and environmental factors. This study presents a machine learning approach tailored specifically to submarine DAS data to enable automated seismic event detection and P- and S-wave identification. Leveraging DeepLab v3, a neural network architecture optimized for semantic segmentation, we developed a specialized model to handle the unique challenges of submarine DAS data. Our model was trained and validated on a data set comprising nearly 57 million manually and semi-automatically labelled seismic records from multiple globally distributed submarine sites, providing a robust basis for accurate seismic detection. The model adapts to a variety of deployment scenarios and can process DAS data from cables with different lengths, configurations and channel spacings, making it versatile for various ocean environments. We thus provide an adaptable and efficient tool for automated earthquake analysis of DAS data, which has the potential to enhance real-time earthquake monitoring and tsunami early warning in submarine environments.