SVR-DAS: A Machine-Learning-Based Method to Create Earthquake Catalogs from Seafloor Distributed Acoustic Sensing Measurements

Seafloor earthquake monitoring is one of the emergent research areas in seismology. Its importance relies on the continuous monitoring of earthquake activity near or on subduction zones, where large megathrust earthquakes are generated. In Japan, seafloor observation systems have been deployed to monitor earthquake activity in Nankai area (DONET) and Japan Trench (S-net). However, these are limited to the number of observation points of the system, and their installation and maintenance can be logistically and economically expensive. Distributed Acoustic Sensing (DAS) measurements obtained from fiber optic cables are arising as a promising technology to monitor offshore earthquake activity. In addition, DAS data is suitable for deep learning methods due to the vast volume of data that it produces. Although successful methods have been developed for land-based DAS measurements, their performance in seafloor DAS data is compromised by additional sources of noise and coupling issues. State-of-the-art research is aimed at developing novel deep-learning-based methods to process seafloor data. However, DAS-based earthquake catalogs are still scarce, and manual picking is not feasible. In this work, we propose a novel machine-learning-based method to build earthquake catalogs from seafloor DAS measurements. Our method detects earthquakes by applying envelope template matching using a small number of templates obtained from DAS data itself. P and S arrival times are obtained from detections using STA/LTA and Autoregressive models using the Akaike Information Criterion (AR-AIC). The coincident picks of both methods are used as training data to create P and S picks models using Support Vector Regression (SVR). For this reason, our method is called SVR-DAS. We test the method using relatively high Signal-to-Noise Ratio (SNR) DAS data from an earthquake that occurred in 2023-07-26. Subsequently, we applied SVR-DAS to the DAS data from 2022-02-28, obtaining P and S models from relatively high SNR DAS data. The created catalogs are used to train and validate a CNN-RNN model. In addition, we present a comparison of the SVR-DAS picks with the picks obtained from PhaseNet-DAS, a deep-learning-based model for earthquake detection and seismic picking. Our results prove that SVR-DAS is capable to build earthquake catalogs from DAS data with enough accuracy. The obtained catalogs are useful for further development of deep learning models for seafloor DAS data processing.