Based on the microseismic monitoring data of underground cavern of Shuangjiangkou Hydropower Station, the internal relationship between rockburst and microseismic events is analyzed and the occurrence mechanism of slip-type rockburst in underground cavern of Shuangjiangkou Hydropower Station is analyzed through the change of microseismic monitoring b value to warn the risk level of rockburst. The results show that microseismic events are closely related to excavation activities, and the change of b value reflects the rockburst risk level. The smaller the b value of microseismic events is, the more large magnitude events is and the greater the possibility of rockburst is. The slip-type rockburst was mainly affected by structural plane; it is due to the accumulation of elastic strain energy at the structural plane and the sudden release under the action of unloading or disturbance, driving the block on the structure surface fast slide, mainly manifested as the sliding of large blocks of surrounding rock, and forming a “V” shape pit blasting or cuneate blasting crater, as well as destruction with crackle. Compared with the strained rockburst, the damage is more serious. The research results can provide reference for rockburst prediction and prevention in similar deep rock engineering.

Research on Temporal and Spatial Distribution Characteristics of Microseismic Events of Slip-Type Rockburst

Due to the significant acoustic impedance contrast between bone and soft tissue, it is difficult to image bone with conventional ultrasound imaging modalities based on uniform sound velocity. Accurate estimation of the sound velocity distribution is the key basis for bone ultrasonic imaging. The sound velocity model is usually estimated using travel-time inversion based on the extraction of the time-of-flight (TOF). However, TOF extraction is an intractable task for bone imaging, since the first arrival signal is usually of a small amplitude and submerged in noise. To improve the accuracy of TOF extraction and further guarantee the accuracy of velocity inversion, a segmentation method using convolutional neural network was proposed in this work. The TOF extraction was translated into a binary segmentation problem and solved by the data-driven method. After sound velocity inversion, a velocity-corrected version of the Kirchhoff migration method (MKMM) was developed to reconstruct bone image. The performance of the proposed method was verified by numerical bone models and ex-vivo bovine femurs coated with soft-tissue phantoms. The results demonstrated that the proposed method provided more accurate TOF extractions (i.e., the errors < 0.24 μs for numerical models and < 0.4 μs for ex-vivo specimens). The resulting velocity inversion was significantly improved (the mean relative errors < 4.5% for numerical models and < 5.1% for ex-vivo samples). Moreover, the satisfactory reconstructions of bone and the overlying soft tissue can be observed. It is demonstrated that the proposed method is effective for bone ultrasonic imaging.

Deep Learning for TOF Extraction in Bone Ultrasound Tomography

In microseismic monitoring, achieving an accurate and efficient first-arrival picking is crucial for improving the accuracy and efficiency of microseismic time-difference source location. In the era of big data, the traditional first-arrival picking method cannot meet the real-time processing requirements of microseismic monitoring process. Using the advanced idea of deep learning-based end-to-end classification and the prominent feature extraction advantages of a fully convolution neural network, this paper proposes a first-arrival picking method of effective signals for microseismic monitoring based on UNet++ network, which can significantly improve the accuracy and efficiency of first-arrival picking. In this paper, we first introduced the methodology of the UNet++-based picking method. And then, the performance of the proposed method is verified by the experiments with finite-difference forward modeling simulated signals and actual microseismic records under different signal-to-noise ratios, and finally, comparative experiments are performed using the U-Net-based first-arrival picking algorithm and the Short-Term Average to Long-Term Average (STA/LTA) algorithm. The results show that compared to the U-Net network, the proposed method can obviously improve the first-arrival picking accuracy of the low signal-to-noise ratio microseismic signals, achieving significantly higher accuracy and efficiency than the STA/LTA algorithm, which is famous for its high efficiency in traditional algorithms.

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Xiaolong Guo

Papers

First-Arrival Picking for Microseismic Monitoring Based on Deep Learning