Method of Automatically Detecting the Abnormal First Arrivals Using Delay Time (December 2020)

Qin, Ziyu; Pan, Shulin; Chen, Jingyi; Cui, Qiu; He, Jiang

doi:10.1109/tgrs.2021.3118921

Cited by 2 publications

(1 citation statement)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, it also includes some other methods, such as machine learning [21], cluster analysis [22,23], cross-correlation [24], neural networks [25], deep learning [26,27], etc. The time difference localization method relies on accurate algorithms for picking, such as STA/LTA [28], ultrasonic transmission [29], Akaike information criterion [30], C-Means clustering [31], delay time [32], wavelet transform [33], etc. Of course, if the event is interfered with by background noise or other factors, filtering measures should be taken to reduce noise or remove interference [34].…”

Section: Introductionmentioning

confidence: 99%

Waveform Complexity and Positioning Analysis of Acoustic Emission Events during the Compression Failure Process of a Rock Burst Prone Sample

Zhang,

Yu,

et al. 2024

Buildings

View full text Add to dashboard Cite

The localization results of acoustic emission (AE) events can reflect the location and pattern of burst-prone rock failures. However, event localization heavily depends on the quality of the original waveform of the sensor. Therefore, this study analyzed the AE waveform of a rock sample under compression to evaluate its failure localization and quality. From the research results, it could be seen that the initial failure was relatively calm, with clear take-off points, which can be better used for accurate AE event positioning. However, the later failure was severe, causing the take-off points of most sensors to be very unclear, and positioning methods that rely on take-off points cannot be used for positioning, let alone simply using the positioning results of the built-in software. This research result reminds researchers who use AE signals for event localization to first examine the quality and status of the original waveform, providing a basis for obtaining accurate localization results, in order to further accurately study the subsequent failure patterns. The above facts indicate that the initial failure is small and scattered, while the later failure is large and concentrated, with certain fractal characteristics.

show abstract