## 3 Application and Experiment Test  
### Dataset  
To evaluate the efficacy of the proposed CGO-ANN model in predicting PPV induced by mine blasting, we conducted a case study at the Tonglushan Copper Mine in China, as depicted in Figure 4. The geological structure of the area is primarily characterized by the skarn deposit, comprising copper-iron ore, skarn, granodiorite, marble, and other constituents [46; 47]. Given the robustness of the rock mass, blasting has been adopted as the principal and efficient method for rock and ore fragmentation in this mine. However, as highlighted in the introduction, its associated adverse effects, particularly ground vibration, are well recognized. For the development of PPV predictive models, we gathered and updated data from 180 blasting events conducted at the Tonglushan Copper Mine. Our study considered blasting parameters and rock properties as influential factors affecting PPV, consistent with recommendations from previous researchers [38; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57]. We collected 11 input variables for PPV prediction, including maximum explosive charged per delay (Q\({}_{\text{max}}\)), total explosive charged per blast (Q\({}_{\text{total}}\)), monitoring horizontal distance (D), vertical distance (VD), burden (B), pre-crack penetration rate (PPR), delay time (DT), rock mass integrity coefficient (RMI), Protodiakonov's hardness (f), angle between the least resistance line direction and monitoring points (\(\alpha\)), and velocity of detonation (VoD). PPVs were recorded by seismographs within a range of 0.101 cm/s to 8.15 cm/s. GPS devices were utilized to ascertain the positions of seismographs and blast sites. Subsequently, horizontal and vertical distances, along with \(\alpha\), were computed based on the GPS results. Rock properties such as PPR, RMI, and f were determined in the laboratory, while the remaining parameters were extracted from the blast patterns. Table 1 provides a summary of some statistical criteria of the dataset, and Figure 5 illustrates visualizations of the dataset. Figure 4: Tonglushan Copper Mine in China. \begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline \multicolumn{1}{c}{\(\mathbf{Q_{max}}\)} & \multicolumn{1}{c}{\(\mathbf{Q_{total}}\)} & \multicolumn{1}{c}{\(\mathbf{D}\)} & \multicolumn{1}{c}{\(\mathbf{VD}\)} & \multicolumn{1}{c}{\(\mathbf{B}\)} & \multicolumn{1}{c}{\(\mathbf{PPR}\)} \\ \hline Min.: & 160 & Min.: & 936 & Min.: & 28.40 & Min.: & 1.1 & Min.: & 4.000 & Min.: & 25.0 \\
1 st Qu.: & 494 & 1 st Qu.: & 2552 & 1 st Qu.: & 96.03 & 1 st Qu.: & 32.1 & 1 st Qu.: & 5.000 & 1 st Qu.: & 41.0 \\ Median: & 1076 & Median: & 3952 & Median: & 141.40 & Median: & 53.0 & Median: & 5.000 & Median: & 50.0 \\ Mean: & 1268 & Mean: & 4195 & Mean: & 174.18 & Mean: & 54.8 & Mean: & 5.389 & Mean: & 58.2 \\
3 rd Qu.: & 1636 & 3 rd Qu.: & 5600 & 3 rd Qu.: & 234.38 & 3 rd Qu.: & 73.0 & 3 rd Qu.: & 6.000 & 3 rd Qu.: & 75.0 \\ Max.: & 5590 & Max.: & 9000 & Max.: & 444.30 & Max.: & 109.3 & Max.: & 7.000 & Max.: & 100.0 \\ \hline
**DT** & & **RMI** & & **f** & & **\(\boldsymbol{\alpha}\)** & & **VoD** & & **PPV** & \\ \hline Min.: & 0.00 & Min.: & 0.3000 & Min.: & 5.000 & Min.: & 0.00 & Min.: & 2800 & Min.: & 0.1010 \\
1 st Qu.: & 0.00 & 1 st Qu.: & 0.4700 & 1 st Qu.: & 6.000 & 1 st Qu.: & 76.25 & 1 st Qu.: & 2800 & 1 st Qu.: & 0.3862 \\ Median: & 1.50 & Median: & 0.5550 & Median: & 6.000 & Median: & 130.00 & Median: & 2800 & Median: & 1.0380 \\ Mean: & 30.96 & Mean: & 0.5656 & Mean: & 6.489 & Mean: & 121.03 & Mean: & 3430 & Mean: & 1.6262 \\
3 rd Qu.: & 68.50 & 3 rd Qu.: & 0.6725 & 3 rd Qu.: & 7.000 & 3 rd Qu.: & 180.00 & 3 rd Qu.: & 4200 & 3 rd Qu.: & 2.4065 \\ Max.: & 100.00 & Max.: & 0.8000 & Max.: & 9.000 & Max.: & 180.00 & Max.: & 4200 & Max.: & 8.1580 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the blast-induced ground vibration dataset at the Tonglushan Copper Mine. Figure 5: _Cont._Figure 5: Visualization of the blast-induced ground vibration dataset at the Tonglushan Copper Mine.