A Practical Approach to Automatic Earthquake Catalog Compilation in Local OBS Networks Using Deep-Learning and Network-Based Algorithms

In land-based seismology, modern automatic earthquake detection and phase picking algorithms have already proven to outperform classic approaches, resulting in more complete catalogs when only taking a fraction of the time needed for classic methods. For marine-based seismology, similar advances have not been made yet. For ocean-bottom seismometer (OBS) data, additional challenges arise, such as a lower signal-to-noise ratio and fewer labeled data sets available for training deep-learning models. However, the performance of available deep-learning models has not yet been extensively tested on marine-based data sets. Here, we apply three different modern event detection and phase picking approaches to an ∼12 month local OBS data set and compare the resulting earthquake catalogs and location results. In addition, we evaluate their performance by comparing different subcatalogs of manually detected events and visually revised picks to their automatic counterparts. The results show that seismicity patterns from automatically compiled catalogs are comparable to a manually revised catalog after applying strict location quality control criteria. However, the number of such well-constrained events varies between the approaches and catalog completeness cannot be reliably determined. We find that PhaseNet is more suitable for local OBS networks compared with EQTransformer and propose a pick-independent event detection approach, such as Lassie, as the preferred choice for an initial event catalog compilation. Depending on the aim of the study, different schemes of manual repicking should be applied because the automatic picks are not yet reliable enough for developing a velocity model or interpreting small-scale seismicity patterns.