Research Catalog of Inland Seismicity in the Southern Korean Peninsula from 2012 to 2021 Using Deep Learning Techniques

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS ACS Applied Bio Materials Pub Date : 2023-12-13 DOI:10.1785/0220230246
Jongwon Han, Keun Joo Seo, Seongryong Kim, Dong-Hoon Sheen, Donghun Lee, Ah-Hyun Byun
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Abstract

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.
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使用深度学习技术的 2012 至 2021 年朝鲜半岛南部内陆地震研究目录
我们为朝鲜半岛南部(SKP)的内陆和近海岸地区提出了一份跨度为 2012-2021 年的地震目录。利用深度学习(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 多个地震台站收集到的大量数据(跨度达 10 年)。为了提高该框架的性能,我们使用来自 SKP 的本地数据集和全球获得的数据对 DL 挑拣器(即 EQTransformer)进行了重新训练。使用机器学习算法通过相位关联共检测到 66858 个事件,基于 DL 的事件判别模型将 29371 个事件归类为天然地震。我们根据应用 DL 挑选器获得的数据,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)和震级参数(局部震级方程),更精确地估算了震源信息。与之前的目录相比,拟议的目录在统计完整性方面有所改进,多探测到 21,475 个地震。通过新探测和定位的地震,我们观察到北部 SKP 的地震活动性相对较低,地震呈东北-西南(NE-SW)和西北-东南(NW-SE)的线性趋势,两者之间的夹角接近直角。特别是,东北-西南走向与 SKP 主要构造区域的边界相对应,这可能表明沿边界断层结构的发展。两种主要趋势与建议的最佳断层方向略有不同,这意味着原有地质构造的形成过程更为复杂。这项研究证明了基于 DL 的框架在分析大型数据集和探测地震不活跃地区的许多微地震方面的有效性,这将推进我们对稳定大陆地区的地震构造和地震危险的理解。
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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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