<p>Earthquake monitoring has been stepped up due to high-density permanent networks in the southern Korean Peninsula, though its relatively low seismicity. With the dramatic increase of data volume, deep learning techniques can be effective ways to process them. In this study, we present a preliminary, but comprehensive earthquake catalog in the southern Korean Peninsula for research purposes by applying a series of deep learning-incorporated methods including for earthquake and phase detection, event discrimination, and focal mechanism determination. We first improved the EQTransformer by re-training it with hybrid local and STEAD datasets to perform earthquake and phase detection for 10-year-long data from 2012 to 2021. Then, the subsequent phase association was carried out using the algorithm based on a Bayesian gaussian mixture model. In the result, 66,855 events were identified and located from 691,077 phase detections. Among them, 27,429 natural earthquakes were separated with a novel CNN model trained using event waveforms and origin time constraints. The natural seismicity suggested various earthquake clusters that constrained by tectonic structures, such as the Okcheon belt and Gyeongsang basin, and showed significantly low rate of occurrence in the Gyeonggi massif. In addition, we developed a CNN model for the determination of focal mechanisms that identify the polarity of initial P-waves in input waveforms, and the application of it resulted in 2,345 reliable solutions. Strike-slip motions were dominant in the inland, while reverse faulting of coastal earthquakes, showing an average P-axis direction of N74E in both areas. Despite the massive volume of data, it took less than a week to perform all of the processes with more cataloged earthquakes than those in the previous one (9,218). The extended earthquake catalog accompanied by focal mechanisms underpins data-driven studies such as tomography, stress field estimation, earthquake hazards assessment, and burial fault mapping.</p>
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