Estimation of the Relative Arrival Time of Microseismic Events Based on Phase-Only Correlation

Abstract

The arrival time of a microseismic event is an important piece of information for microseismic monitoring. The accuracy and efficiency of arrival time identification is affected by many factors, such as the low signal-to-noise ratio (SNR) of the records, the vast amount of real-time monitoring records, and the abnormal situations of monitoring equipment. In order to eliminate the interference of these factors, we propose a method based on phase-only correlation (POC) to estimate the relative arrival times of microseismic events. The proposed method includes three main steps:(1) The SNR of the records is improved via time-frequency transform, which is used to obtain the time-frequency representation of each trace of a microseismic event.(2) The POC functions of all pairs of time-frequency representations are calculated. The peak value of the POC function indicates the similarity of the traces, and the peak position in the time lag axis indicates the relative arrival times between the traces. (3) Using the peak values as weighting coefficients of the linear equations, consistency processing is used to exclude any abnormal situations and obtain the optimal relative arrival times. We used synthetic data and field data to validate the proposed method. Comparing with Akaike information criterion (AIC) and cross-correlation, the proposed method is more robust at estimating the relative arrival time and excluding the influence of abnormal situations.

A Method for Rockburst Prediction in the Deep Tunnels of Hydropower Stations Based on the Monitored Microseismicity and an Optimized Probabilistic Neural Network Model

“…Microseismic (MS) monitoring techniques involving the three-dimensional monitoring of MS events produced by the microcracking of rocks have been widely used around the world for many years to monitor and predict rockbursts, with different degrees of success [8,[14][15][16][17][18][19][20][21][22][23][24][25][26]. For example, Feng et al [14] proposed a dynamic method of warning of rockburst development processes in tunnels based on monitored microseismicity.…”

Optimization and Quality Assessment of Arrival Time Picking for Downhole Microseismic Events

“…Many automatic processing workflows have already been developed for arrival-time picking using multi-trace cross-correlation, such as using over-determined linear equations to obtain the optimal result [27,30] or the iterative cross-correlation based method [10,33,34]. However, it is difficult to satisfy the conditions that both similarity coefficients of waveforms in the event and among events are greater than the threshold value in the actual data.…”

“…Estimating time delays among received signals is fundamental for multi-level algorithms. Several techniques are used to estimate the time delay, such as the cross-correlation method [23][24][25][26] and phase-only correlation method [27].…”

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

“…As an advanced spatial 3D technology, microseismic monitoring has been widely exploited in the transportation field, hydropower projects, and the extraction and storage of various types of energy, such as mining engineering, slope engineering, underground and tunnel engineering, shale-gas exploitation, and hot dry rock reservoirs [1][2][3][4][5][6]. The ability to accurately trace microseismic events can be employed to judge the process by which fracture networks develop within rock masses [7][8][9]. Combining seismological theory with the analysis of various seismic data (such as seismic deformation, stress adjustment, energy release, etc.)…”