# Predicting Models for Local Sedimentary Basin Effect Using a Convolutional Neural Network
## 1 Introduction
However, due to the complexity of the structure and soil of real basins, only approximate or simplified models can be used, so it is necessary to study the laws of the amplification mechanisms of different types of basins, to establish the relationship between the parameters and the amplification characteristics, and to improve the existing methods, taking into account the site's effect. In order to analyze the significance of different factors on the basin amplification effect, based on the reasonably simplified standard basin model, the influence of each factor on the ground motion is analyzed by changing the parameters of the numerical simulation method. [PERSON] et al. [9], based on the numerical modeling of sedimentary basins with different dimensions, studied in detail the relationship between the ground motion response of surface stations and their own geometry, the quality factor of the soil layer medium, and the impedance ratio by changing the depth-to-width ratio of the model. [PERSON] et al. [10] popularized the indirect boundary integral equation method to solve the scattering of plane waves and SV waves by a mountainous terrain in a half-space and found that the scattering characteristics of plane waves in a mountainous terrain were greatly related to the incident angle, frequency, and characteristics of the mountainous medium. The comparison of different simplified analysis models provides a basis for describing a site's response reliably using as few parameters as possible. [PERSON] et al. [11] established a basin model of the lower Vale valley in France and studied the influence of geometric model simplification on wave propagation by using the discontinuous Galerkin finite element method. [PERSON] et al. [12, 13] calculated the seismic response of a series of 2D Greek Volvi basin models with different levels of detail in order to capture the nature of their seismic response. It is a representative research idea to use a simplified model that reflects the characteristics of ground motion amplification, rather than using a complex real sedimentation basin model. The influence of the site effect on ground motion can be roughly estimated using ground motion prediction equations, which are primarily based on the observation data obtained from existing stations. These equations extract relevant parameters and reflect the influence of the site effect through simple regression. However, as one of the typical local complex sites, the small- and medium-sized basins are characterized by the undulating shape of the bedrock at the bottom, soil properties, lateral inhomogeneity, and surface topography, which are all factors leading to abnormal ground motion [14], and have complex seismic wave propagation characteristics. Because the observed data are not rich enough and the relative scale is small, the effect of the regression prediction can hardly reflect the local change characteristics of the ground motion. How to reasonably reflect the influence of local site effect in the ground motion prediction of the basin is a problem that needs to be solved to ensure the project's safety. In recent years, with the popularity of artificial intelligence, machine learning has been increasingly applied to the field of civil engineering due to its ability to directly acquire information from data, without assuming the obedience law or internal relationship between data or parameters in advance. For example, in the area of automatic diagnosis of concrete arch beam damage, a method based on optimized deep stacked self-encoders and multi-sensor fusion is proposed, which is important in terms of reducing labor costs and risks [15]. An approach based on an improved bird flocking algorithm and 2D convolutional neural networks is used to assess the torsional capacity of reinforced concrete beams. Accurate and fast torsional capacity assessment is achieved through optimal feature selection and model training [16]. In the area of earthquake engineering, [PERSON] et al.