# Sequence Deep Learning for Seismic Ground Response Modeling: 1D-CNN, LSTM, and Transformer Approach
## 2 Earthquake Data  
We utilized the ground motion data from [PERSON] et al. [32], which employ the strong-motion seismograph network Kiban Kyoshin Network (KiK-net) [36] in Japan. This network, established by the National Research Institute for Earth Science and Disaster Prevention (NIED), comprises seismometers installed in boreholes and on the surface at various sites (marked with black hollow circles in Figure 1) across the country to record earthquake motions. Each KiK-net site provides information about site-specific data, acceleration history measurements, and ground properties essential for seismic ground response analysis.  
Figure 1: Selected earthquake sites for the analysis (reproduced from [PERSON] et al. [32]).  
[PERSON] et al. [32] focus on ground motion data from 12 selected sites (marked with purple-colored circles in Figure 1). Each site has 100 earthquake events recorded after 2008, with moment magnitudes greater than 6 and peak ground accelerations (PGAs) exceeding 0.01 g. These thresholds are to ensure more complete waveforms and a broader range of recorded ground motions as well as to focus on impactful seismic activities.  
The shear wave velocity and density profiles for these sites are presented in Figure 2. Table 1 summarizes the average shear wave velocity (\(V_{s,30}\)), average natural period (\(T_{g}\)), and the corresponding National Earthquake Hazard Reduction Program (NEHRP) site classification based on \(V_{s,30}\) and \(T_{g}\) for each site. For a more comprehensive description including the ground layer profiles obtained from borehole data, please refer to [PERSON] et al. [32].  
The earthquake datasets include acceleration time history measurements at the bedrock (100 m below the ground surface) and the ground surface. Using these time history data, we generate acceleration, velocity, and displacement response spectra with a five percent damping ratio. The deep learning models in this study take the response spectra (acceleration, velocity, and displacement) at the bedrock as the input and predict the surface  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Site ID** & **Site Name** & \(V_{s,30}\) (m/s) & \(T_{g}\) (s) &
\begin{tabular}{c} **NEHRP Site** \\ **Classification** \\ \end{tabular} & **Description** \\ \hline FKSH17 & Kawamata & 544 & 0.22 & C & Dense soil, soft rock \\ FKSH18 & Miharu & 307.2 & 0.39 & D & Stiff soil \\ FKSH19 & Miyakoji & 338.1 & 0.35 & D & Stiff soil \\ IBRH13 & Takahagi & 335.4 & 0.36 & D & Stiff soil \\ IWTH02 & Tamayama & 816.3 & 0.15 & B & Rock \\ IWTH05 & Fujisawa & 442.1 & 0.27 & C & Dense soil, soft rock \\ IWTH12 & Kunhoe & 367.9 & 0.33 & C & Dense soil, soft rock \\ IWTH14 & Taro & 816.3 & 0.15 & B & Rock \\ IWTH21 & Yamada & 521.1 & 0.23 & C & Dense soil, soft rock \\ IWTH22 & Towa & 532.1 & 0.23 & C & Dense soil, soft rock \\ IWTH27 & Rikuzentakata & 670.3 & 0.18 & C & Dense soil, soft rock \\ MYGH04 & Towa & 849.8 & 0.14 & B & Rock \\ \hline \hline \end{tabular}
\end{table}
Table 1: The information of the selected sites (reproduced from [PERSON] et al. [32]).  
Figure 2: The shear wave velocity (reproduced from [PERSON] et al. [32]) and the density profile for the selected sites.  
acceleration response spectrum. The following section provides a detailed explanation of the models.