OBSPicker: A generalized transfer-learned OBS phase picker

Offshore earthquakes recorded by Ocean Bottom Seismometers (OBS) are crucial to studying tectonic activities in the subduction zones and mid-ocean ridges. In recent years, the ever-advancing Machine Learning (ML)-based phase pickers have shown promise in land earthquake monitoring, but there are few available ML models to handle OBS data due mainly to the lack of labelled training sets and low signal-to-noise ratios. Though land ML-based phase pickers may roughly work for OBS data, they introduce a large number of false negatives and false positives, leading to numerous events being missing and fake.In this study, we create a tectonically inclusive OBS training data set and develop a generalized deep-learning OBS phase picker - OBSPicker using the EQTransformer (EQT; Mousavi et al., 2019) and the transfer learning approach. To create an inclusive OBS training data set, we collect earthquake waveforms from routine catalogues recorded at 11 OBS networks worldwide with different tectonic settings and geographic locations. Earthquakes are recorded in local and regional distances with diverse magnitudes (ML 0.0-5.8), source depths (0-250 km), and epicentral distances (0-3 deg). To label their phase picks, we adopt a sequence of processing steps including 1) initial phase arrival detection and picking by EQT, 2) identifying and discarding samples with multiple (unwanted) events using STA/LTA method, and 3) refining phase picks using the Generalized Phase Detection method (GPD, Ross et al., 2018), resulting in ~38,000 well-labelled earthquake samples. In addition, we also collect ~150,000 OBS noise samples from the same OBS networks for training augmentation instead of using the commonly adopted Gaussian noises. Those OBS noise samples are used to simulate low-magnitude earthquakes under different marine environments. Initial results show that our transfer-learned OBS phase picker outperforms the EQTransformer base model in both accuracy and precision, especially in presence of higher levels of noise.

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