A seismicity catalog spanning 2012–2021 is proposed for the inland and near‐coastal areas of the southern Korean Peninsula (SKP). Using deep learning (DL) techniques combined with conventional methods, we developed an integrated framework for compiling a comprehensive seismicity catalog. The proposed DL‐based framework allowed us to process, within a week, a large volume of data (spanning 10 yr) collected from more than 300 seismic stations. To improve the framework’s performance, a DL picker (i.e., EQTransformer) was retrained using the local datasets from the SKP combined with globally obtained data. A total of 66,858 events were detected by phase association using a machine learning algorithm, and a DL‐based event discrimination model classified 29,371 events as natural earthquakes. We estimate source information more precisely using newly updated parameters for locations (a 1D velocity model and station corrections related to the location process) and magnitudes (a local magnitude equation) based on data derived from the application of the DL picker. Compared with a previous catalog, the proposed catalog exhibited improved statistical completeness, detecting 21,475 additional earthquakes. With the newly detected and located earthquakes, we observed the relative low seismicity in the northern SKP, and the linear trends of earthquakes striking northeast–southwest (NE–SW) and northwest–southeast (NW–SE) with a near‐right angle between them. In particular, the NE–SW trend corresponds to boundaries of major tectonic regions in the SKP that potentially indicates the development of fault structures along the boundaries. The two predominant trends slightly differ to the suggested optimal fault orientations, implying more complex processes of preexisting geological structures. This study demonstrates the effectiveness of the DL‐based framework in analyzing large datasets and detecting many microearthquakes in seismically inactive regions, which will advance our understanding of seismotectonics and seismic hazards in stable continental regions.

Temporary seismic networks on Mt. Halla and Ulleung Island volcanoes were deployed, which employ broadband and geophone arrays to monitor potential volcanic activities and to estimate high-resolution magmatic structures beneath these volcanoes. The purpose of this paper is to introduce these networks and present early results through basic seismic analyses, suggesting the potential for future comprehensive seismological studies. The array in Mt. Halla volcano consists of five broadband sensors (JH array), and it has been operational around the Baengnokdam summit crater since October 2020. There was an additional linear geophone array (HL array) installed in September 2021 for detailed shallow subsurface imaging. Ulleung Island volcano had been under observation for two years since June 2021 with a network of nine broadband sensors (UL array) along its coast and in the Nari crater basin, complemented by a 52-geophone array (UG array) deployed in May 2022 for high-resolution subsurface studies. Despite the noisy environments typical of temporary setups, power spectral density analyses confirmed the quality of data as comparable to established reference noise models in permanent stations. Our study aimed to initiate studies uncovering seismic activities and structures beneath Mt. Halla and Ulleung Island volcanoes, specifically regarding volcanic activity. This approach detected no clear sign of volcanic seismicity on both islands, suggesting a period of magmatic dormancy. Seismic velocity variation (dv/v) analyses further indicated that environmental factors, rather than volcanic processes, influenced the changes in the physical properties of the underground structures. Conversely, the receiver function analysis and ambient noise data processing hinted at the presence of complex subsurface structures, potentially indicative of volcanic features, such as partial melting. Despite the lack of direct evidence for active magmatic processes, the collected seismic data provides a crucial baseline for future monitoring and a deeper understanding of the magmatic and tectonic dynamics beneath these volcanoes, offering valuable insights for ongoing volcanic research. 

On 3 May 2020, an ML 3.1 earthquake occurred in Haenam, southwestern Korea, in an area devoid of recorded seismicity since instrumental observations began in 1978. Careful examination of the temporal occurrence of seismicity, and the magnitude distribution of the sequence before and after the ML 3.1 earthquake, indicates typical swarm-like behavior. The earthquake swarm started with an ML 0.6 event on 26 April 2020, intensified up to 3 May 2020, and abruptly terminated with an ML 1.0 event on 9 May 2020. The Pusan National University Geophysics Laboratory (PNUGL) deployed a temporary seismic array with eight three-component short-period instruments to monitor the short-lived bursts of seismicity. During the monitoring campaign, we detected > 700 microearthquakes by applying a matched-filter technique to the combined dataset produced by PNUGL, the Korea Meteorological Administration, and the Korea Institute of Ocean Science and Technology. We determined earthquake parameters for 299 earthquakes that were detected at four or more seismic stations. We also determined the focal mechanism solutions of the 10 largest earthquakes in the swarm using first-motion polarities with S/P ratios. The focal mechanism, hypocentral depth, and stress orientation of the largest earthquake in the sequence were also determined using waveform inversions. The distribution of earthquake hypocenters, together with focal mechanism solutions, indicates that the earthquake swarm activated deeply-buried faults (~20 km) oriented either NNE-SSW or WNW-ESE. We also report details of the temporary seismic monitoring network, including the instrumentation, detection of microearthquakes, and variations in event-detection threshold influenced by anthropogenic and natural noise fluctuations. We also discuss the limitations associated with lowering the detection threshold of microearthquakes by increasing the number of seismic stations or by adopting advanced event-detection techniques.