Global Wave Velocity Change Measurement of Rock Material by Full-Waveform Correlation

Zhou, Jing; Zhou, Zilong; Zhao, Yuan; Cai, Xun

doi:10.3390/s21227429

Cited by 3 publications

(1 citation statement)

References 41 publications

(55 reference statements)

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“…With the advantage of lower dependency on the alignment of the observed seismograms, this method greatly reduced the impact of poorly modeling velocity structures [25]. The third way is to use stochastic approaches to quantitatively calculate the uncertainties of the full moment tensor (ISO and non-DC components) when using a simplified velocity model, e.g., by perturbing the dataset (e.g., randomly removing a certain partial of the available traces) to derive the probability density distribution of the moment tensors and to infer a likelihood interpretation [43,44].…”

Section: Introductionmentioning

confidence: 99%

Cooperative P-Wave Velocity Measurement with Full Waveform Moment Tensor Inversion in Transversely Anisotropic Media

Zhao

et al. 2022

Sensors

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Precise stochastic approaches to quantitatively calculate the source uncertainties offers the opportunity to eliminate the influence of anisotropy on moment tensor inversion. The effects of ignoring anisotropy were tested by using homogeneous Green’s functions. Results indicate the influence of anisotropy and noise on fault plane rotation is very small for a pure shear source whether it is restricted to double couple solution or full moment tensor solution. Green’s functions with different prior rough anisotropy information were tested, indicating that the complex source is more sensitive to velocity models than the pure shear source and the fault plane rotation caused by full moment tensor solution is larger than the pure double couple solution. Collaborative P-wave velocity inversion with active measurements and passive acoustic emission data using the fast-marching method were conducted, and new Green’s functions established based on the tomography results. The resolved fault plane solution rotated only 3.5° when using the new Green’s functions, but the presence of spurious isotropic and compensated linear vector dipole components was not completely eliminated. It is concluded that the cooperative inversion is capable of greatly improving the accuracy of the fault plane solutions and reducing the spurious components in the full moment tensor solution.

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Section: Introductionmentioning

confidence: 99%

Cooperative P-Wave Velocity Measurement with Full Waveform Moment Tensor Inversion in Transversely Anisotropic Media

Zhao

et al. 2022

Sensors

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Real-Time Identification of Rock Failure Stages Using Deep Learning: A Case Study of Acoustic Emission Analysis in Rock Engineering

Pu,

Chen,

Apel

et al. 2024

Preprint

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The accurate and timely determination of rock failure processes is crucial for various rock engineering applications, especially for preventing dynamic disasters such as rock bursts and roof failures. The primary aim of this study was to determine the current rock failure stage using a single acoustic emission (AE) event signal recorded during the failure process. To achieve this, we proposed a deep learning model that employs advanced convolutional modules and a soft-threshold technique to extract the full waveform features of AE events from four different stages of rock failure in a laboratory uniaxial compressive strength (UCS) test. Once fully trained, our model can instantaneously determine the current rock failure stage from a raw waveform of a single recorded AE event. Subsequently, the trained model was applied to on-site microseismic data analysis at a coal mine working face. Compared to traditional methods of microseismic data analysis that consider large-energy events, our model can identify the rock failure stage at the time of a specific microseismic event. Furthermore, by analyzing microseismic events triggered by post-peak rock fracturing, we identified potential hazard areas for rock bursts in the working face, and the results closely matched the site's burst prevention logs. This study successfully developed a real-time method for determining rock failure stages using deep learning, which can be effectively applied to microseismic data analysis in engineering sites to provide more precise early warnings of rock dynamic disasters.

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An Improved Method for Calculating Wave Velocity Fields in Fractured Rock Based on Wave Propagation Probability

Zhou,

Liu,

Zhao

et al. 2024

Mathematics

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Ultrasonic velocity field imaging offers a robust tool for characterizing and analyzing damage and its evolution within fractured rock masses. The combined application of ultrasonic first arrival waves and coda waves can significantly enhance the accuracy and range of velocity field imaging. This manuscript introduces an improved imaging method that integrates the propagation probability distribution of the first arrival and coda waves to calculate the velocity field. The proposed method was applied to the velocity field imaging of a medium with multiple scatterers and varying degrees of fracturing. The overall error and calculation unit error of the proposed method were analyzed, and its improvement in calculation accuracy and applicable scope was verified. Additionally, this method was employed to image the velocity field during the damage process of fractured rock masses. The imaging results were compared against digital speckle patterns to confirm the method’s suitability. Finally, we discussed the impact of measurement errors and sensor missing on the accuracy of the computational outcomes presented in this method. These two situations will affect the calculation results, and the influence of reducing the number of sensors is smaller than that of measuring time shifts with error.

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Global Wave Velocity Change Measurement of Rock Material by Full-Waveform Correlation

Cited by 3 publications

References 41 publications

Cooperative P-Wave Velocity Measurement with Full Waveform Moment Tensor Inversion in Transversely Anisotropic Media

Cooperative P-Wave Velocity Measurement with Full Waveform Moment Tensor Inversion in Transversely Anisotropic Media

Real-Time Identification of Rock Failure Stages Using Deep Learning: A Case Study of Acoustic Emission Analysis in Rock Engineering

An Improved Method for Calculating Wave Velocity Fields in Fractured Rock Based on Wave Propagation Probability

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