## 5 Discussions  
### 3D Green's Function Database Modeling  
In waveform inversion, the 3D Green's function database modeling for a single source point with the finest grid (\(10\times 10\times 10\) m\({}^{3}\)) is about 120 cpu-hours. Especially when the full-space grid search strategy is adopted, the workload of modeling the Green's functions from all candidate source grid points to sensors is directly proportional to the total number of spatial grid points. Therefore, this study submitted parallel tasks on local clusters with 2 eight-cores Intel E5-2680 2.7 GHz processors and introduced the reciprocity theorem into wavefield modeling in a mine, which can rapidly compute the Green's functions using fewer global simulations. The current supercomputing resources can finish the construction of the 3D Green's function database with fine grid for a whole mining region in two or three days. To further improve the computational efficiency, the workload of waveform inversion and \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{6}{c}{**Event Location (m)**} & \multicolumn{6}{c}{**Location Error (m)**} \\ \cline{2-9}
**Event id** & **X** & **Y** & **Z** & **[PERSON] et al. [56]** & **[PERSON] et al. [57]** & **[PERSON] et al. [19]** & **[PERSON] et al. [6]** & **[PERSON] et al. [7]** & **GSWI Location** \\ \hline
1 & 381,683 & 2,997,760 & 1107 & 84.8 & 61.1 & 31.1 & 32.6 & 23.0 & 25.0 \\
2 & 381,653 & 2,997,405 & 1099 & & 22.4 & 21.1 & 14.8 \\
3 & 381,194 & 2,996,224 & 1014 & 27.6 & 28.7 & 49.4 & 14.3 & 5.1 & 14.0 \\
4 & 381,684 & 2,997,777 & 1107 & 3878.2 & & 30.8 & 29.8 & 24.2 & 15.6 \\
5 & 381,503 & 2,997,206 & 1028 & & & 28.5 & 7.2 & 7.8 \\
6 & 381,509 & 2,997,208 & 1053 & & 70.1 & 52.4 & 49.0 & 30.8 & 23.8 & 21.5 \\
7 & 381,526 & 2,997,584 & 1044 & & 55.0 & 48.8 & 24.5 & 18.2 & 6.7 \\
8 & 381,422 & 2,998,029 & 1017 & & 143.5 & & 42.8 & 26.6 & 13.8 & 14.6 \\ Average & & & & & 709.9 & 47.4 & 42.0 & 26.2 & 17.0 & 15.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Location errors of the eight premeasured blasting events using different location methods. storage requirements of the [PERSON]'s function database are reduced by the multi-scale mesh modeling strategy that is similar to [PERSON] et al. [63]. The next steps can be realized through a laptop at a quick calculation. If there is more prior knowledge about the regions where MS events occur frequently, the combination of reciprocity theorem and multi-scale grid search could further reduce the wavefield modeling workload and storage requirement of the [PERSON]'s function database effectively, which can be generalized for simultaneous multi-source locating in the future. In addition, under the current limitations of single-component sparse monitoring network and dataset with insufficient signal-to-noise ratio, we have not been able to obtain an appropriate elastic parameter model better than the currently used isotropic Vp tomographic model. Therefore, pure scalar acoustic wave modeling with a rough or simple velocity model is used (corresponding to acoustic pressure field records) to carry out studies in many current exploration-scale microseismic applications. However, the simplified velocity model is not accurate enough in the estimation of the strength of velocity anomalies and geometrical characteristic of the strong structure heterogeneities, such that the next stage task is to further improve the resolution of the structure parameter model. According to the complicated relationship between the spatial resolution of the target model and various control factors, the waveform inversion aiming at the broadband waveform dataset (such as joint tomography or full waveform inversion) can be performed based on accurate wavefield modeling considering that the practical waveform records presents a limited frequency band distribution. High-resolution structure parameter waveform inversion approach considers the wavefield finite frequency effect to improve the model resolution.