Quantitative Evaluation of the “Non-Enclosed” Microseismic Array: A Case Study in a Deeply Buried Twin-Tube Tunnel

Abstract

The high-stress hazards of underground engineering have stimulated the exploration of microseismic monitoring and early warning methods. To achieve a good monitoring effect, the monitoring object is usually enclosed by a microseismic array (sensor array) (e.g., slope engineering, etc.). However, some characteristics of a buried tunnel, including “linear”, “deep-buried”, and “long”, make it difficult to deploy a reasonable microseismic array, which leads to the microseismic array being non-enclosed for the monitoring object. Application of the non-enclosed microseismic array yields decreases the accuracy of the source location. To solve the problem wisely, this paper deals with the feasibility of non-enclosed microseismic arrays (axial-extended, lateral-extended, and twin-tube arrays) by introducing a quantitative method. To this end, an optimized microseismic array with the best source location accuracy for a twin-tube expressway tunnel is proposed. The obtained results reveal that the non-enclosed microseismic arrays, which are unavoidable in expressway tunnel engineering, do not introduce errors but reduce the ability to resist them. Further, the twin-tube array achieves a better source location accuracy than the axial and lateral-extended arrays. In the application of the source location based on the particle swarm optimization (PSO) algorithm to the twin-tube array, microseismic events, which cluster in the rockburst section, are wholly gathered, and the maximum error is reduced by about 30–50 m, indicating its greater feasibility with respect to the single-tube array.

Multi-Classification of Complex Microseismic Waveforms Using Convolutional Neural Network: A Case Study in Tunnel Engineering

“…Noisy signals with different SNR values were generated by scaling the noise amplitude (Figure 6). The detail calculation of the SNR is as follows [33]: SNR = 20 × log 10 (S Amax /N Amax ) (7) where S Amax and N Amax are the peak amplitudes of the signal and noise, respectively. completely and accurately detect the microseismic signals with a SNR higher than 2 dB.…”

“…As a new real-time monitoring technology of rock mass stability, microseismic monitoring technology has been extensively applied in tunnel, mines, slopes, and other dynamic disaster early warning system projects [1][2][3][4][5][6][7][8][9]. This method can help effectively evaluate the current fracture status of surrounding rocks by analyzing the microseismic data recorded during monitoring, and then help further evaluate and predict the potential risk areas of rock masses.…”

An Anisotropic Velocity Model for Microseismic Events Localization in Tunnels

“…Tunnel engineering construction projects typically follow a linear path, and microseismic events frequently occur in proximity to the tunnel face; most occur beyond the scope of the sensor array. Thus, compared to conventional microseismic monitoring techniques, locating microseismic events in tunnel engineering is challenging [ 10 , 11 ]. In microseismic event localization, the commonly used methods are based on interferometry and time difference of arrival (TDOA) [ 12 , 13 , 14 , 15 ].…”

Effect of the Initial Support of the Tunnel on the Characteristics of Rockburst: Case Study and Mechanism Analysis

“…e microseisms determine the failure characteristics and mechanism of rockburst [19][20][21][22][23][24]. To date, the interactive mechanism between microseismic activity and supporting structure remains less investigated, and the microseismic activity is rarely used to guide support strategy.…”