한국지진공학회논문집 (Journal of the Earthquake Engineering Society of Korea)

  • 제25권2호
  • /
  • Pages.71-81
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  • 2021
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  • 1226-525X(pISSN)
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  • 2234-1099(eISSN)
한국지진공학회 (Earthquake Engineering Society of Korea)
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온사이트 지진조기경보를 위한 딥러닝 기반 실시간 오탐지 제거

Deep Learning-Based, Real-Time, False-Pick Filter for an Onsite Earthquake Early Warning (EEW) System

  • 서정범 (케이아이티밸리 AiLab) ;
  • 이진구 (케이아이티밸리 AiLab) ;
  • 이우동 (전북대학교 지구환경과학과) ;
  • 이석태 (케이아이티밸리 AiLab) ;
  • 이호준 (케이아이티밸리 연구기획본부) ;
  • 전인찬 (케이아이티밸리 AiLab) ;
  • 박남률 (케이아이티밸리 AiLab)
  • 투고 : 2020.12.31
  • 심사 : 2021.02.14
  • 발행 : 2021.03.01
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초록

This paper presents a real-time, false-pick filter based on deep learning to reduce false alarms of an onsite Earthquake Early Warning (EEW) system. Most onsite EEW systems use P-wave to predict S-wave. Therefore, it is essential to properly distinguish P-waves from noises or other seismic phases to avoid false alarms. To reduce false-picks causing false alarms, this study made the EEWNet Part 1 'False-Pick Filter' model based on Convolutional Neural Network (CNN). Specifically, it modified the Pick_FP (Lomax et al.) to generate input data such as the amplitude, velocity, and displacement of three components from 2 seconds ahead and 2 seconds after the P-wave arrival following one-second time steps. This model extracts log-mel power spectrum features from this input data, then classifies P-waves and others using these features. The dataset consisted of 3,189,583 samples: 81,394 samples from event data (727 events in the Korean Peninsula, 103 teleseismic events, and 1,734 events in Taiwan) and 3,108,189 samples from continuous data (recorded by seismic stations in South Korea for 27 months from 2018 to 2020). This model was trained with 1,826,357 samples through balancing, then tested on continuous data samples of the year 2019, filtering more than 99% of strong false-picks that could trigger false alarms. This model was developed as a module for USGS Earthworm and is written in C language to operate with minimal computing resources.

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