# Seismic Ground Response Estimation Based on Convolutional Neural Networks (CNN)
## 1 Introduction  
One of the main purposes of modern earthquake engineering is to mitigate damage in buildings and infrastructure and, therefore, reduce the impact of earthquakes on society [1]. When an earthquake occurs at fault, the earthquake waves propagate from the source to other sites. The seismic waves travel through rocks over most of their trip from the source to the ground surface, and they finally reach the surface through the soil. As the soil deposits tend to act as filters to seismic waves by amplifying motion at certain frequencies and attenuating it at others, characteristics of the soil mainly influence the nature of shaking at the ground surface, which is called the site effect [2, 3]. Evaluating the local site effect on ground shaking is an essential part of earthquake-resistant design [4]. Seismic ground response analysis refers to the process of evaluating the ground surface motions based on the bedrock motion to consider the local site effect [5]. One-dimensional seismic ground response analyses with wave equations have been widely performed in practices [6].  
Since [PERSON] and [PERSON] (1989) published the first article on applications of neural networks to structural design [7], a large number of research have been conducted on civil engineering applications of neural networks. In particular, convolutional neural networks (CNN), a class of artificial neural networks commonly applied to analyze visual imagery, can extract features from raw data [8]. [PERSON] et al. (2018) detected earthquake occurrences and locations of epicenters in a predefined area with seismic waveformsrecorded on a seismic station using CNN [9]. [PERSON] et al. (2018) trained the CNN model on the vast data archives of the Southern California Seismic Network to detect seismic body-wave phases [10]. [PERSON] et al. (2019) introduced the CNN-Recurrent neural networks Earthquake detector (CRED), a combination of convolutional layers and bi-directional long-short-term memory units in a residual structure [11]. [PERSON] and [PERSON] (2019) proposed a CNN-based approach to evaluate the dynamic response of a linear single-degree-of-freedom (SDOF) system, a nonlinear SDOF system, and a full-scale, three-story, multi-degree-of-freedom (MDOF) steel frame [12]. [PERSON] et al. (2021) presented a review on the application of soft computing techniques for the rapid visual safety evaluation and damage classification of an existing building [13]. [PERSON] et al. (2021) utilized CNN to tune the first approximation to predict the maximum interstory drift ratio (MIDR) [14].  
Although research on the application of artificial neural networks to structural design has been actively conducted, the application to the seismic ground response analysis is insufficient. This study suggests two convolutional neural networks (CNN) models, which estimate the seismic response of the surface based on the seismic motion measured at the bed rock in a site, and selects the one which outperforms the other as the main model. The performances of the main model in each site are compared with those of conventional analysis software for every selected site.