인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Abstract Recent advances in deep learning have enhanced our ability to analyze seismic waveforms. Here, we developed and evaluated a convolutional neural network (CNN) model to classify tectonic tremors, earthquakes, and noise in seismic waveform data recorded by a seismic array in the Nankai subduction zone. The trained CNN model achieved high accuracy, with both precision and recall exceeding 97%, and correctly detected 96% of distant earthquakes. The probability of tectonic tremor as a function of the signal-to-noise ratio (SNR) increased steeply from 10 to 90% at an SNR of 4. We highlighted tectonic tremor waveforms using the integrated gradients (IG) method for interpreting CNN models. IG filter averaging over the stations of an array outperforms bandpass filters and other interpretation methods for CNN models in locating tectonic tremors by semblance analysis, providing the largest number of tectonic tremor sources. As reported previously, located sources of tectonic tremor during episodic tremor and slip events migrate along the strike of the subducting plate. The source location error increases significantly at epicentral distances greater than 30 km because of low SNRs. The technique developed in this study equips CNN models with a high ability to distinguish tectonic tremors and earthquakes from noise and to locate tectonic tremors with sources that are not far from seismic stations. Graphical abstract
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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