A rapid and automatic procedure for seismic analysis based on deep learning and template matching: a case study on the M 4.1 Goesan earthquake on October 29, 2022

As various seismological tasks with deep learning have been implemented, it has been reported that fundamental seismic analysis processes were replaced with deep learning techniques while minimizing the intervention of skilled analysts. In addition, the template matching method using the GPU (Graphic Processing Unit) architecture has enabled effective detection of smaller earthquakes than previously known. In this study, we analyzed a magnitude 4.1 earthquake that occurred in Goesan-gun, South Korea, around 8:27:49 on October 29, 2022, and foreshocks and aftershocks of this earthquake in an automated way using deep learning and template matching techniques. Using deep learning techniques, seismograms from permanent seismic stations within 50 km of the epicenter were used to create an initial earthquake catalog for 11 days from October 21 to October 31, 2022. It was found that a total of 50 events, including 25 events published by the Korea Meteorological Administration, occurred from the morning of October 29th. The focal mechanisms of 10 events, including the mainshock of magnitude 4.1, were automatically determined, and all of them were characterized by a strike-slip faulting mechanism. By cross-correlating waveforms of template events, it was identified that more than 500 micro-earthquakes near the hypocenter occurred for few days from October 29, 2022. These tasks took only about 70 minutes to generate an initial earthquake catalog of 11-day seismograms and about 10 minutes for template matching. To investigate seismic activity in this region, template matching with the same templates was carried out with data from March 18, 2020 to October 20, 2022. The result shows that more than four micro-earthquakes have occurred since May 2020. Therefore, this study suggests that the automatic seismic analysis procedure using deep learning and template matching can quickly deliver important information about earthquakes in the early stages.