## 3 Results and Discussion
### Prediction Results of CNN-PSPA Models
#### 3.3.2 Comparisons with Observed Results  
The non-LHES model, with the best test results among all the six CNN-PSPA models, was selected for predicting all the site amplification factors of 39 earthquakes at the LHES station location, where five earthquake records were not included. The prediction results for the 39 earthquakes are listed in Table 11. The site amplification factors obtained from  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & **Predicted** & \multirow{2}{*}{**Kernel Size**} & **Kernel** & \multirow{2}{*}{**Ave. Error**} & **Error** \\  & **Station** & & **Number** & **Ave. Error** & **Fluctuation** \\ \hline \multirow{4}{*}{non-SOCS} & \multirow{4}{*}{SOCS} & [2 1] [3 1] & 190 310 & 16.5\% & \multirow{4}{*}{8.7\%} \\  & & [2 1] [3 1] & 190 315 & 18.5\% & \\  & & [2 1] [3 1] & 190 318 & 14.2\% & \\  & & [2 1] [3 1] & 190 320 & 22.9\% & \\ \hline \multirow{4}{*}{non-PGMS} & \multirow{4}{*}{PGMS} & [2 1] [3 1] & 190 320 & 23.1\% & \multirow{4}{*}{9.1\%} \\  & & [2 1] [3 1] & 196 310 & 19.0\% & \\  & & [2 1] [3 1] & 196 320 & 24.5\% & \\  & & [2 1] [3 1] & 198 320 & 15.4\% & \\ \hline \multirow{4}{*}{non-TAIS} & \multirow{4}{*}{TAIS} & [2 1] [3 1] [4 1] & 190 320 322 & 16.5\% & \multirow{4}{*}{4.3\%} \\  & & [2 1] [3 1] [4 1] & 190 320 325 & 18.6\% & \\  & & [2 1] [3 1] [4 1] & 190 320 330 & 17.9\% & \\  & & [2 1] [3 1] [4 1] & 192 320 330 & 20.8\% & \\ \hline \multirow{4}{*}{non-FAIS} & \multirow{4}{*}{FAIS} & [2 1] [3 1] & 190 315 & 22.4\% & \multirow{4}{*}{9.8\%} \\  & & [2 1] [3 1] & 190 320 & 12.6\% & \\  & & [2 1] [3 1] & 196 320 & 13.2\% & \\  & & [2 1] [3 1] & 190 326 & 17.9\% & \\ \hline \multirow{4}{*}{non-LHES} & \multirow{4}{*}{LHES} & [2 1] [3 1] & 188 320 & 22.1\% & \multirow{4}{*}{9.9\%} \\  & & [2 1] [3 1] & 190 320 & 12.2\% & \\  & & [2 1] [3 1] & 192 320 & 16.0\% & \\  & & [2 1] [3 1] & 196 320 & 21.6\% & \\ \hline \multirow{4}{*}{non-BMIS} & \multirow{4}{*}{BMIS} & [2 1] [3 1] & 190 320 & 21.1\% & \multirow{4}{*}{3.9\%} \\  & & [2 1] [3 1] & 192 320 & 22.4\% & \\ \cline{1-1}  & & [2 1] [3 1] & 192 330 & 25.0\% & \\ \cline{1-1}  & & [2 1] [3 1] & 196 320 & 23.6\% & \\ \hline \hline \end{tabular}
\end{table}
Table 10: Results for CNN-PSPA with different parameters.  
the observation records are listed in Table 11 for comparison. By comparing with the corresponding observation results, the errors were also calculated for all the incidents, as shown in Table 11.  
Table 11 shows that the total average prediction error was 12.2%, which was relatively low. In addition, it can clearly be seen from Table 11 that most of the prediction errors for small earthquakes such as M4.0 and M4.5 were rather high, probably due to the effect of anomalous amplification of seismic waves by the soil layer. In contrast, the predictions for the medium-sized earthquakes such M5.1 and M5.6 were better. Furthermore, low prediction errors for stations FAIS, PGMS and SOCS (but not the bedrock station LHES) were observed. This indicates that the CNN-FSPA model showed good training for those three stations. Compared with the CNN-FSPA model in Table 9, the average error of the CNN-PSPA model was slightly higher by 3.7%.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multirow{2}{*}{**Earthquake**} & \multirow{2}{*}{**Epicentral**} & \multirow{2}{*}{**Depth**} & \multicolumn{4}{c}{**CNN-PSPA**} \\ \cline{5-8}  & & & **(km)** & **(km)** & **Observed** & **Predicted** & **Error** & **Error** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 11: Prediction results for non-LHES.  
In addition, the errors for small earthquakes with large epicenter and source depths, such as earthquakes with serial numbers 1, 3, 7 and 16, were significantly higher than those for other earthquakes. However, for medium earthquakes with large epicentral distances and focal depths, such as earthquakes with serial numbers 20, 32 and 39, errors were lower and predictions were satisfactory.