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.

中文翻译：

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利用深度学习技术的2012年至2021年朝鲜半岛南部内陆地震活动研究目录

提出了朝鲜半岛南部（SKP）内陆和近海岸地区2012-2021年地震活动目录。使用深度学习（DL）技术与传统方法相结合，我们开发了一个用于编制综合地震活动目录的集成框架。所提出的基于深度学习的框架使我们能够在一周内处理从 300 多个地震台收集的大量数据（跨越 10 年）。为了提高框架的性能，使用 SKP 的本地数据集与全局获取的数据相结合来重新训练 DL 选择器（即 EQTransformer）。使用机器学习算法通过相位关联检测到总共 66,858 个事件，基于深度学习的事件判别模型将 29,371 个事件分类为自然地震。我们使用新更新的位置参数（一维速度模型和与定位过程相关的台站校正）和幅度（局部幅度方程），基于从深度学习选择器应用中获得的数据，更精确地估计源信息。与之前的目录相比，拟议的目录显示出更高的统计完整性，检测到了 21,475 次额外的地震。根据新发现和定位的地震，我们观察到SKP北部的地震活动性相对较低，并且地震呈东北-西南（NE-SW）和西北-东南（NW-SE）的线性趋势，两者之间呈近直角。他们。特别是，NE-SW 趋势对应于 SKP 中主要构造区域的边界，这可能表明沿边界的断层结构的发育。这两个主要趋势与建议的最佳断层方向略有不同，这意味着先前存在的地质结构的过程更加复杂。这项研究证明了基于深度学习的框架在分析大型数据集和检测地震不活跃地区的许多微地震方面的有效性，这将增进我们对稳定大陆地区地震构造和地震灾害的理解。