## 3 Results and Discussion
### Prediction Results of the CNN-FSPA Model
#### 3.2.2 Comparisons with Recorded Results  
With the two convolutional layers shown in Table 8, the CNN-FSPA model gave better prediction results than the other models with different parameters. Therefore, the selected CNN-FSPA model was used to predict the amplification factors of the seven known station locations. The prediction results for earthquakes with magnitudes of 4.0, 4.5, 5.1 and 5.6 are listed in Table 9. The comparisons of the predicted and observed results are also shown in Table 9 and Figure 11.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Layer**} & \multirow{2}{*}{**Type**} & \multirow{2}{*}{**Kernel No.**} & **Kernel** & **Stride** & **Padding** & **Activation** \\ \hline
1 & Input & None & None & None & None & None \\
2 & Convolution (C1) & 190 & \(2\times 1\) & 1 & 1 & Leaky ReLU \\
3 & Convolution (C2) & 320 & \(3\times 1\) & 1 & 1 & Leaky ReLU \\
4 & FC & None & None & None & None & None \\
5 & Output & None & None & None & None \\ \hline \hline \end{tabular}
\end{table}
Table 8: Structural parameters of the 1-D CNNs.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Station**} & \multirow{2}{*}{**Earthquakes**} & \multirow{2}{*}{**Observed**} & \multirow{2}{*}{**Predicted**} & \multirow{2}{*}{**Error**} & **Average** \\  & & & & & \\ \hline \multirow{4}{*}{LHRS} & M4.5 & 1.00 & 1.02 & 2.0\% & \\  & M5.6 & 1.00 & 0.99 & 1.0\% & \\  & M5.1 & 1.00 & 1.02 & 2.0\% & \\  & M4.0 & 1.00 & 1.03 & 2.9\% & \\ \hline \multirow{4}{*}{BMITS} & M4.5 & 2.45 & 1.79 & 26.9\% & \\  & M5.6 & 1.78 & 1.89 & 5.8\% & \\  & M5.1 & 2.01 & 1.76 & 12.4\% & \\  & M4.0 & 1.61 & 1.55 & 3.7\% & \\ \hline \multirow{4}{*}{LHES} & M4.5 & 2.05 & 2.23 & 8.1\% & \\  & M5.6 & 2.56 & 2.69 & 5.1\% & \\  & M5.1 & 2.81 & 2.93 & 4.1\% & \\  & M4.0 & 3.42 & 2.66 & 22.2\% & \\ \hline \multirow{4}{*}{FAIS} & M4.5 & 2.86 & 2.30 & 19.6\% & \\  & M5.6 & 1.90 & 1.95 & 2.6\% & \\ \cline{1-1}  & M5.1 & 1.91 & 1.94 & 1.5\% & \\ \cline{1-1}  & M4.0 & 1.81 & 1.88 & 3.7\% & \\ \hline \multirow{4}{*}{TAIS} & M4.5 & 3.56 & 3.08 & 13.5\% & \\ \cline{1-1}  & M5.6 & 3.10 & 3.15 & 1.6\% & \\ \cline{1-1}  & M5.1 & 3.65 & 3.23 & 11.5\% & \\ \cline{1-1}  & M4.0 & 2.98 & 2.37 & 20.5\% & \\ \hline \multirow{4}{*}{PGMS} & M4.5 & 1.83 & 2.10 & 12.9\% & \\ \cline{1-1}  & M5.6 & 1.95 & 2.04 & 4.4\% & \\ \cline{1-1}  & M5.1 & 2.06 & 2.02 & 1.9\% & \\ \cline{1-1}  & M4.0 & 2.06 & 1.72 & 16.5\% & \\ \hline \multirow{4}{*}{SOCS} & M4.5 & 2.50 & 2.61 & 4.2\% & \\ \cline{1-1}  & M5.6 & 3.04 & 3.00 & 1.3\% & \\ \cline{1-1}  & M5.1 & 3.24 & 3.16 & 2.5\% & \\ \cline{1-1}  & M4.0 & 3.27 & 2.57 & 21.4\% & \\ \hline \hline \end{tabular}
\end{table}
Table 9: Prediction results for the CNN-FPSA.  
Table 9 shows that the total average prediction error was 8.5%, which is relatively low. In addition, it can clearly be seen from Table 9 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 of the middle-sized earthquakes such as M5.1 and M5.6 were better. Furthermore, it can be observed that the stations FAIS, PGMS and SOCS (but not the bedrock station LHES) had low prediction errors. This indicates that the CNN-FSPA model had good testing results for those three stations.  
As shown in the Figure 11, the scatter points were evenly and regularly distributed around the black oblique line. This shows that this model was able to provide a good prediction.