High-Accuracy Location of Microseismic Events in a Strong Inhomogeneous Mining Environment by Optimized Global Full Waveform Inversion

Abstract

High-accuracy determination of a microseismic (MS) location is the core task in MS monitoring. In this study, a 3D multi-scale grid Green’s function database, depending on recording wavefield frequency band for the target mining area, is pre-generated based on the reciprocity theorem and 3D spectral element method (SEM). Then, a multi-scale global grid search strategy is performed based on this pre-stored Green’s function database, which can be effectively and hierarchically processed by searching for the spatial location. Numerical wavefield modeling by SEM effectively overcomes difficulties in traditional and simplified ray tracing modeling, such as difficult wavefield amplitude and multi-path modeling in 3D focusing and defusing velocity regions. In addition, as a key step for broadband waveform simulation, the source-time function estimated from a new data-driven singular value decomposition averaged fractional derivative based wavelet function (DD-SVD-FD wavelet) was proposed to generate high-precision synthetic waveforms for better fitting observed broadband waveform than those by simple and traditional source-time function. Combining these sophisticated processing procedures, a new robust grid search and waveform inversion-based location (GSWI location) approach is integrated. In the synthetic test, we discuss and demonstrate the importance of 3D velocity model accuracy to waveform inversion-based location results for a practical MS monitoring configuration. Furthermore, the average location error of the 3D GSWI location for eight real blasting events is only 15.0 m, which is smaller than error from 3D ray tracing-based location (26.2 m) under the same velocity model. These synthetic and field application investigations prove the crucial role of 3D velocity model, finite-frequency travel-time sensitivity kernel characteristics and accurate numerical 3D broadband wavefield modeling for successful MS location in a strong heterogeneous velocity model that are induced by the presence of ore body, host rocks, complex tunnels, and large excavations.

Securing Fast and High-Precision Localization for Shallow Underground Explosive Source: A Curiosity-Driven Deep Reinforcement Learning Approach

“…Duan et al developed a P-and PS-wavefront imaging method to estimate the source origin time sequentially [19]. Wang et al integrated a grid search and waveform inversionbased location approach in [20]. While the authors in [21], [22], and [23]…”

“…The distributed shallow underground explosive source localization system developed by the Key Laboratory of Shanxi Province of Information Detection and Processing, North University of China, was used to obtain the localization parameters. The monitoring area of 120m*120m*60m (l*w*h) was divided into small cube grids with the same size [21], [48], and the grid width was set as 1 m [19], [20]. The range of x-axis, yaxis, and z-axis were [-60,60], [-60,60], and [-60,0], respectively.…”

“…Table III presents the localization results of three experiments. According to the difference of the preset source locations, the search starting point of the source (10,45,-30) and (40,-20,-50) were set to (25,20,0) and (20,-20,0) respectively. The search times were also set as 100.…”

A Feasibility Study on Microseismic Monitoring of Rock Burst in Traffic Tunnels

“…Localization of microseismics is at the core of microseismic monitoring [83][84][85]. In this process, the microseismic monitoring system inferred the location of the rupture after processing the signals accordingly.…”

An Energy Focusing-Based Scanning and Localization Method for Shallow Underground Explosive Sources

“…Wavefront tomography also requires a wide range of high-density sensor arrays with a sufficient aperture for tomography inversion to obtain a high-precision velocity model; otherwise, it may lead to source localization failure [19,20]. The full waveform inversion (FWI) localization method is used to acquire the source location by minimizing the residual between the observed waveform and the synthesized waveform to complete waveform matching [21,22]. However, FWI requires continuous forward modeling and velocity updates, which have a high computational cost and require a more accurate initial velocity model.…”