为了建立地震动峰值加速度的概率分布模型，收集了500个台站的255365条地震动记录，建立了初步的峰值加速度统计样本。以广义极值分布作为地震动峰值加速度的概率模型；利用既定的广义极值分布模型，对常用于估算极值分布模型参数的最大似然估计法和线性矩估计法的有效性进行了分析。基于最大似然估计的渐近正态性，提出了建立广义极值分布模型时确立所需最小样本长度的方法，分析表明建立地震动峰值加速度的广义极值分布模型时数据样本长度不宜小于120。对符合样本长度要求的地震动峰值加速度数据样本进行统计分析，发现其模型参数会随着样本长度的增加而收敛于一个较小的范围，最后建立了不同类型场地的实测地震动峰值加速度的概率统计模型和地震危险性计算公式。

In order to establish a probability distribution model of peak ground acceleration, 255,365 ground motion recordings were collected from 500 stations to create initial statistical samples of peak ground acceleration. The generalized extreme value distribution was employed as the probability model for peak ground acceleration. The effectiveness of the maximum likelihood estimation method and the linear moment estimation method, commonly used for estimating parameters of the extreme value distribution model, was assessed using the established generalized extreme value distribution model. Based on the asymptotic normality of the maximum likelihood estimation, a method was proposed to determine the minimum required sample length when establishing a generalized extreme value distribution model. The analysis indicated that, when constructing the generalized extreme value distribution model for peak ground acceleration in seismic events, the data sample size should not be less than 120. Statistical analysis was conducted on seismic peak ground acceleration data samples that met the sample size requirement. It was observed that the model parameters converged to a relatively narrow range as the sample size increased. Ultimately, probability statistical models for measured peak ground acceleration for different types of sites were established, along with seismic hazard calculation formulas.