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This study evaluates ground-motion (GM) acceleration conversion methods by applying them to strain-rate data from a horizontal Distributed Acoustic Sensing (DAS) array under both idealized and real-world conditions. We test four conversion methods—1) slant-stacking, 2) Lior’s method, 3) Lindsey’s method, and 4) Curvelet transform—through numerical modeling and by applying them to a publicly available 9 km horizontal DAS array dataset. Numerical simulations reveal critical calculation factors specific to each method and show that numerically derived apparent ground velocity can deviate from theoretical values when multiple elastic waves arrive simultaneously. In real-world applications, the slant-stacking and Lior’s methods are relatively insensitive to the measurement length of the straight DAS array. By contrast, the Curvelet method exhibits strong sensitivity to this factor, whereas Lindsey’s method shows weaker dependence. Implementing Lior’s method in the frequency-wavenumber domain also requires pre-determining water-levels by comparing adjacent seismograms. Additionally, we find that Lior’s method generates excessively high spectral levels above 13 Hz, which may lead to underestimation of the high-frequency spectral attenuation parameter (κ0), a key parameter in GM simulation. Collectively, these findings provide a technical guideline for the use of horizontal DAS arrays in future observational earthquake seismology.

This study evaluates ground-motion (GM) acceleration conversion methods by applying them to strain-rate data from a horizontal Distributed Acoustic Sensing (DAS) array under both idealized and real-world conditions. We test four conversion methods—1) slant-stacking, 2) Lior’s method, 3) Lindsey’s method, and 4) Curvelet transform—through numerical modeling and by applying them to a publicly available 9 km horizontal DAS array dataset. Numerical simulations reveal critical calculation factors specific to each method and show that numerically derived apparent ground velocity can deviate from theoretical values when multiple elastic waves arrive simultaneously. In real-world applications, the slant-stacking and Lior’s methods are relatively insensitive to the measurement length of the straight DAS array. By contrast, the Curvelet method exhibits strong sensitivity to this factor, whereas Lindsey’s method shows weaker dependence. Implementing Lior’s method in the frequency-wavenumber domain also requires pre-determining water-levels by comparing adjacent seismograms. Additionally, we find that Lior’s method generates excessively high spectral levels above 13 Hz, which may lead to underestimation of the high-frequency spectral attenuation parameter (κ0), a key parameter in GM simulation. Collectively, these findings provide a technical guideline for the use of horizontal DAS arrays in future observational earthquake seismology.

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기계 학습 알고리즘을 적용한 국내 철근콘크리트 건물의 구조 유형 분류 = Structural type classification of domestic reinforced concrete buildings utilizing machine-learning algorithms 김태완 p. 111-121
수평방향 분산진동센서 변형률 자료의 지반가속도 변환 방법에 대한 연구 = Study on the ground-motion acceleration conversion methods from the horizontal strain-rate array data of distributed acoustic sensing 연관희, 최승훈, 윤병준 p. 123-134
동적수치해석을 이용한 액상화 기초지반 위 철도제방의 제방높이와 입력주파수에 따른 침하량 평가 = Settlement evaluation of railway embankments on liquefiable foundation soil according to embankment height and input frequency using dynamic numerical analysis 오이태, 하익수 p. 135-142

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