Deep Learning P and S wave phase picking of Ocean Bottom Seismometer (OBS) data

Abstract

<p>Ocean Bottom Seismometers (OBS) are the primary instruments used in the study of marine seismicity. Due to the characteristics of their emplacement on the sea bottom, these instruments have a much lower signal-noise ratio than land seismometers. Therefore, difficulties arise on the analysis of the data, specially when using automatic methods.</p><p>During recent years the use of machine learning methods applied to seismic signal analysis has increased significantly. We have developed a neural network algorithm that allows to pick seismic body signals, allowing to correctly identify P and S waves with a precision higher than 98%. This network was trained using data of the Southern California Seismic Network and was applied satisfactorily in analysis of data from Large-N experiments in different regions from Europe and Asia.</p><p>One of the remarkable characteristics of the network is the ability to identify the noise, both in the case of seismic signals with low signal-noise ratio and in the case of large amplitude non-seismic signals, such as human-induced noise. This feature makes the network an optimal candidate to study data recorded using OBS.</p><p>We have modified this neural network in order to analyze OBS data from different deployments. Combined with the use of an associator, we have successfully located events with very low signal-noise ratio, achieving results with a precision comparable or superior to a human operator.</p>