# A Machine-Learning-Based Software for the Simulation of Regional Characteristic Ground Motion
## 5 Conclusions  
A regional ground-motion synthesis software was developed in this study. The software extracts characteristic ground motions using PCA and a linear combination of algorithms that perform genetic algorithm optimization for obtaining time history records, satisfying real requirements and verifying their accuracy.  
Combined with the machine learning method, the synthesis method of this procedure considers matching both the target response spectrum and target duration compared to the traditional simulation method, and it satisfies engineering demands for achieving reasonable ground motions. The software also offers regional amplitude fields and time history records for larger scenarios.  
The software was designed entirely in a MATLAB GUI environment. A clear target area can be drawn via the visualization interface and provided parameters (such as seismic magnitude, site, and latitude and longitude of the target area) to improve the user's understanding; furthermore, amplitude intensity fields of the main periods are provided, while the time-history records are generated in the target folder. In addition, the software provides a user-friendly interface for users to obtain artificial regional ground-motion fields as per their requirements.  
Amplitude, spectrum, and duration are the three most important features of ground motion. Amplitude and spectrum are usually matched in engineering. Therefore, we introduce duration as a matching restriction. However, other intensity metrics like Arias intensity, CAV, and PGV are also significant, and we will continue to investigate them in the next works.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Method** & **Ms** & \begin{tabular}{c} **Hypocenter** \\ **(km)** \\ \end{tabular} & \begin{tabular}{c} **Epicentral** \\ **Distance (km)** \\ \end{tabular} & **Vs30** & **D\({}_{5-95}\)** & \
\begin{tabular}{c} **Duration** \\ **Error** \\ \end{tabular} &
\begin{tabular}{c} **Response** \\ **Spectrum Error** \\ \end{tabular} \\ \hline Trigonometric series Method in the paper & 5 & 10 & 10 & 360 & 8.469 & 0.239 & 0.0739 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The time and response spectrum matching of two synthetic methods.  
**Author Contributions:** Conceptualization, [PERSON] and Y.D.; methodology, H.Z.; software, Y.D.; validation, C.J., H.Z. and S.L.; formal analysis, Y.D.; investigation, J.H.; resources, C.J.; data curation, H.Z.; writing--original draft preparation, Y.D.; writing--review and editing, J.H.; visualization, J.H.; funding acquisition, J.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Scientific Research Fund of Institute of Engineering Mechanics, China Earthquake Administration, grant number 2020D08; 2021 EEEVL0103, National Key R&D Program of China, grant number 2018 YFC1504401, and the National Natural Science Foundation of China, grant number U1939210.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data for this study were provided by the China Strong Motion Networks Center (CSMNC) at the Institute of Engineering Mechanics, China Earthquake Administration.  
**Conflicts of Interest:** The authors declare no conflict of interest.