# A Machine-Learning-Based Software for the Simulation of Regional Characteristic Ground Motion
## 1 Introduction  
The time-history analysis method is an important tool for calculating the response of structures under earthquakes. However, time-history analysis relies on reliable ground-motion inputs. In general, there are two types of ground motion--the real ground-motion records obtained by seismic stations in the advent of earthquakes, and synthetic ground motion. Real ground motions are obtained from the ground-motion database [1], whereas several methodologies are employed to obtain synthetic ground motions [2, 3]. Many factors affect the structural response (e.g., load uncertainty, structural material, and construction quality), among which load uncertainty has the highest influence [4, 5], and ground-motion input (as an important part of the structural load input) has a considerable effect. In practice, recorded ground motions are preferred over synthetic ground motions [6]. This is because the synthetic ground motions according to some criteria (e.g., control spectra, etc.) are different from the recorded ground motions to some extent. However, the lack of recorded ground motion makes it impossible to select a suitable record. Therefore, it is important to synthesize reliable and reasonable ground motion for time-history analysis [2]. Ground-motion-synthesis methodologies have been developed for many years, and several researchers have proposed different methods to simulate ground motions based on different principles. Currently, ground-motion-simulation methods are divided intothree categories: (1) Ground-motion simulation based on the stochastic method; (2) physics-based ground-motion-simulation methods; and (3) hybrid simulated ground motions [3, 7, 8, 9, 10, 11]. Numerous techniques for simulating ground motion are currently under investigation [12, 13, 14, 15, 16, 17]. For example, Ref. [18] simulated ground-motion time series at uninstrumented sites using a gaussian process regression. They estimated the time series at a site based on observed ground motions at surrounding sites, and the method can be used for estimating and understanding causes of earthquake damage at uninstrumented sites. The amplitude, spectrum, and duration simultaneously constrain the ground motions. Currently, the most widely used method in engineering is ground-motion synthesis with matching target response spectrum. However, this synthesis method cannot obtain a unique ground-motion time history. Therefore, a multicriteria objective function for ground motion is proposed. Seismic event parameters, target spectra (Sa, haz(T)), target intensity measures (IMs) for a specific site, structure of interest, and other conditions are investigated to simulate more reasonable ground motion [19, 20, 21]. The energy of seismic waves recorded at a station is related to the strong motion part of the recorded ground motion. For a given acceleration a(t), velocity v(t), or displacement d(t), Ref. [29] defined the strong motion part of the recorded ground motion as the fast-growing region of the integral, where f(t) is any of a(t), v(t), or d(t). The time interval at which the maximum contribution of this integral occurs is defined as the strong motion duration. Ref. [29] defined the 5-95% time interval as the ground-motion duration. Therefore, it is commonly used in seismic hazard analysis [18, 19]. However, this is not reflected in the simulation method. In addition, there are regional differences in the amplitude, spectrum, and duration of ground motions due to geological formations [26]. When simulating ground motions using the machine learning method, it is essential to consider the regional characteristics of the location of interest. To ensure accurate results, it becomes necessary to constrain the amplitude, spectrum, and duration as the main control factors. By carefully adjusting these parameters, the simulation can better capture the specific ground-motion characteristics relevant to the location under study.