# Sequence Deep Learning for Seismic Ground Response Modeling: 1D-CNN, LSTM, and Transformer Approach
## 6 Conclusions  
In this study, we explore the potential of sequence deep learning models--specifically 1D-CNN, LSTM, and transformer architectures--for seismic ground response modeling. Using ground motion data from the KiK-net network, we train these models to predict the ground surface acceleration response spectrum from bedrock motions and compare their performance to the traditional physics-based model, SHAKE2000.  
The data-driven models successfully predict the overall trends of the response spectra and the peak amplitude at critical periods, showing consistently lower prediction errors compared to the physics-based model across various sites. Among the models, the transformer model exhibits the smallest average prediction error owing to its ability to capture long-range dependencies in the seismic data with its self-attention mechanism. The LSTM models also demonstrated good performance due to their ability to manage sequential dependencies. The 1D-CNN, while generally effective, occasionally shows higher errors,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Site** & **CNN** & **LSTM** & **Transformer** & **SHAKE** \\ \hline FKSH17 & 0.000129 & 0.000092 & 0.000101 & 0.001697 \\ FKSH18 & 0.000391 & 0.000177 & 0.000156 & 0.009639 \\ FKSH19 & 0.009381 & 0.012412 & 0.009521 & 0.120766 \\ IBRH13 & 0.007119 & 0.009947 & 0.008425 & 0.013708 \\ IWTH02 & 0.002300 & 0.000726 & 0.000830 & 0.004351 \\ IWTH05 & 0.004891 & 0.003666 & 0.003003 & 0.005632 \\ IWTH12 & 0.003959 & 0.002878 & 0.004252 & 0.076996 \\ IWTH14 & 0.000458 & 0.000163 & 0.000139 & 0.001461 \\ IWTH21 & 0.000830 & 0.000689 & 0.001646 & 0.003686 \\ IWTH22 & 0.000529 & 0.000830 & 0.000315 & 0.048460 \\ IWTH27 & 0.000824 & 0.000750 & 0.000536 & 0.002810 \\ MYGH04 & 0.004510 & 0.002340 & 0.002776 & 0.064900 \\ \hline Avg. & 0.002943 & 0.002889 & 0.002642 & 0.029734 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average mean squared error (\(MSE_{\text{avg}}\)) for the models.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**CNN Model (s)** & **LSTM Model (s)** & **Transformer Model (s)** \\ \hline
0.3830 & 0.4087 & 0.3894 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Computation time for the data-driven model.  
probably due to the limited ability to handle the long-range sequential dependencies. All the data-driven models exhibit efficient computation times of less than 0.4 s for estimation. The results provide new insights into the potential of sequence deep learning models for improving seismic ground response estimation accuracy.  
While our results highlight the potential of data-driven approaches for seismic response modeling, the current models are trained for each site to consider the site-specific condition. In other words, if one model that is trained on a specific site is applied to another site for prediction, the model is not likely to consider the site characteristics properly. Our future research will focus on developing an integrated model that can consider the site characteristics across different sites in the prediction.  
Conceptualization, Y.C. ([PERSON]) and J.A.; formal analysis, Y.C. ([PERSON]), H.-T.N., T.H.H., Y.C. ([PERSON]), and J.A.; investigation, Y.C. ([PERSON]), H.-T.N. and T.H.H.; methodology, Y.C. ([PERSON]) and Y.C. ([PERSON]); project administration, J.A.; validation, Y.C. ([PERSON]) and J.A.; writing--original draft, Y.C. ([PERSON]); writing--review and editing, Y.C. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Korea Institute of Marine Science & Technology Promotion (KIMSI) funded by the Ministry of Oceans and Fisheries (20220364), and Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2022 RII1A3069043).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.