• Title/Summary/Keyword: P-wave arrival time determination algorithm

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A Study on the P Wave Arrival Time Determination Algorithm of Acoustic Emission (AE) Suitable for P Waves with Low Signal-to-Noise Ratios (낮은 신호 대 잡음비 특성을 지닌 탄성파 신호에 적합한 P파 도달시간 결정 알고리즘 연구)

  • Lee, K.S.;Kim, J.S.;Lee, C.S.;Yoon, C.H.;Choi, J.W.
    • Tunnel and Underground Space
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    • v.21 no.5
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    • pp.349-358
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    • 2011
  • This paper introduces a new P wave arrival time determination algorithm of acoustic emission (AE) suitable to identify P waves with low signal-to-noise ratio generated in rock masses around the high-level radioactive waste disposal repositories. The algorithms adopted for this paper were amplitude threshold picker, Akaike Information Criterion (AIC), two step AIC, and Hinkley criterion. The elastic waves were generated by Pencil Lead Break test on a granite sample, then mixed with white noise to make it difficult to distinguish P wave artificially. The results obtained from amplitude threshold picker, AIC, and Hinkley criterion produced relatively large error due to the low signal-to-noise ratio. On the other hand, two step AIC algorithm provided the correct results regardless of white noise so that the accuracy of source localization was more improved and could be satisfied with the error range.

Development of an Algorithm for P-wave Arrival Time determination Using Amoving Window Function (가변창문함수를 이용한 미소파괴음의 P파 도달시간 결정 알고리즘 개발)

  • Lee, Kyung-Soo;Cho, Seong-Ha;Lee, Chang-Soo;Choi, Young-Chul;Yoo, Bo-Sun
    • The Journal of Engineering Geology
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    • v.25 no.1
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    • pp.103-113
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    • 2015
  • This study presents a novel algorithm for determining the P-wave arrival time using amoving window function to improve source localization in low-SNR (signal-to-noise ratio)acoustic emissions. The proposed algorithm was applied to low-SNR signals to verify the accuracy of measurements against existing algorithms. When other algorithms were applied, the test results revealed that SNR decreased and accuracy was reduced, especially where SNR wasless than 2.14. The proposed algorithm using amoving window function considers the frequency characteristic and signal amplitude simultaneously, and produced reliable results where SNR was 2.14.