## 4 Results and Discussion
### Prediction Model for Geophysical Logging Response of Coal Quality
#### 4.4.2 Validation of Coal Quality Logging Response Prediction Model  
The neural network model's reliability in predicting coal quality parameters was further validated through application to the No. 7 and No. 8 coal seams of the Shanxi Formation in the Xuzhuang and Kongzhuang mines, northern Jiangsu. For moisture content (M\({}_{\text{ad}}\)), processed \(\rho_{\text{s}}\), GR, and DEN logging values served as input layers. The absolute error between predicted and measured values ranged from \(-0.48\%\) to \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameters** & **AV** & **AVP** & \(p\) & \(t\) & _Sig_ (Two Tailed) \\ \hline \multirow{2}{*}{\(M_{\text{ad}}\)} & MV & 1.685 & 0.430 & 0.126 & 0.901 \\  & PV & 1.677 & & 0.126 & 0.901 \\  & MV & 11.520 & 0.870 & \(-0.413\) & 0.685 \\ \multirow{2}{*}{\(A_{\text{d}}\)} & PV & 12.061 & & \(-0.413\) & 0.685 \\  & MV & 38.578 & 0.098 & 0.978 & 0.336 \\ \multirow{2}{*}{\(V_{\text{daf}}\)} & PV & 38.048 & & 0.978 & 0.340 \\  & MV & 54.105 & 0.354 & 0.156 & 0.878 \\ \multirow{2}{*}{\(FC\)} & PV & 53.893 & & 0.156 & 0.878 \\  & MV & 0.787 & 0.888 & 0.121 & 0.905 \\ \multirow{2}{*}{\(S_{\text{td}}\)} & PV & 0.762 & & 0.121 & 0.905 \\  & MV & 30.297 & 0.014 & 0.688 & 0.498 \\ \multirow{2}{*}{\(Q_{\text{grad}}\)} & PV & 30.089 & & 0.688 & 0.501 \\ \hline \hline \end{tabular}  
* AV, average value; MV, measured value; FV, predicted value; AVP, average value of parameters.  
\end{table} 
Table 2: Two-sample independent \(t\)-test table for predicted and measured coal quality parameters. Figure 12: Correlation coefficient between coal quality indicators and logging parameters. 0.47% (average: 0.03%), with relative errors between \(-21.05\%\) and \(35.88\%\) (average: \(6.41\%\)) (Table 3). As yield (\(\rm A_{d}\)), modeled using DEN, AC, and GR inputs, showed absolute errors of \(-5.12\%\) to \(1.23\%\) (average: \(-1.64\%\)) and relative errors of \(-37.65\%\) to \(8.77\%\) (average: \(-10.88\%\)) (Table 4). Volatile matter (\(\rm V_{d\rm f}\)), predicted via LLD, GR, and AC inputs, exhibited absolute errors of \(-8.65\%\) to \(7.16\%\) (average: \(-0.64\%\)) and relative errors of \(-18.33\%\) to \(22.75\%\) (average: \(-0.10\%\)) (Table 5). Fixed carbon (FC), optimized with LLD, GR, and AC inputs, yielded absolute errors of \(-1.67\%\) to \(2.52\%\) (average: \(0.78\%\)) and relative errors of \(-3.07\%\) to \(5.29\%\) (average: \(1.63\%\)) (Table 6). For total sulfur (\(\rm S_{t\rm d}\)), \(\rm\rho_{s}\), AC, and GR inputs produced absolute errors of \(-0.14\%\) to \(0.04\%\) (average: \(0.03\%\)) and relative errors of \(-15.12\%\) to \(6.78\%\) (average: \(-2.82\%\)) (Table 7). Calorific value (\(\rm Q_{grad}\)), modeled with LLD, GR, and CGS inputs, showed absolute errors of \(-6.18\%\) to \(0.91\%\) (average: \(-1.66\%\)) and relative errors of \(-16.9\%\) to \(3.14\%\) (average: \(-4.72\%\)) (Table 8). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**DHN** & **NCS** & \(N_{\rm DEN}\) & \(N_{\rm GR}\) & \(N_{\rm AC}\) & \(\mathbf{A_{\rm d}\text{-}PV\) (\%)} & \(\mathbf{A_{\rm d}\text{-}MV\) (\%)} & \(\mathbf{AE\) (\%)} & \(\mathbf{RE\) (\%)} \\ \hline KZ-2 & 8 & 0.26 & 0.14 & 0.08 & 10.41 & 13.08 & \(-2.67\) & \(-20.41\) \\ KZ-5 & 7 & 0.58 & 0.44 & 0.04 & 14.43 & 13.32 & 1.11 & 8.33 \\ KZ-8 & 7 & 0.52 & 0.10 & 0.05 & 8.48 & 13.60 & \(-5.12\) & \(-37.65\) \\ XZ-1 & 8 & 0.85 & 0.42 & 0.06 & 15.25 & 14.02 & 1.23 & 8.77 \\ XZ-3 & 8 & 0.82 & 0.72 & 0.09 & 14.56 & 17.00 & \(-2.44\) & \(-14.35\) \\ XZ-7 & 7 & 0.98 & 1.00 & 0.07 & 17.54 & 19.48 & \(-1.94\) & \(-9.96\) \\ \hline \end{tabular}
\end{table} 
Table 4: Error analysis of ash yield in coal.