基于VGG16卷积神经网络模型的川滇地区谱比法场地分类研究

Site Classification by Spectral Ratio Method Based on VGG16 Convolutional Neural Network Model in Sichuan-Yunnan Region

摘要:

传统的谱比法在地震场地分类中存在一定的主观性和不确定性,引入机器学习算法可以通过大量的样本数据和特征提取方法,提高分类方法的客观性和准确性,为地震工程设计和地震危险性评估提供更可靠的依据。本研究利用川滇地区历史地震的强震台事件波形数据,计算不同场地类别强震动记录的水平和竖向速度反应谱值获取谱比曲线,分析了其在卓越周期、峰值和整体幅值上的特征。引入深度学习算法,整理并生成了川滇地区强震动台站谱比曲线样本集,并将其用作为VGG16卷积神经网络模型的输入数据,训练得到了NEHRP下三类标准场地的概率分布模型。通过特征提取和交叉验证算法,提高了模型的精度和泛化能力。研究所建立的场地分类模型在验证结果中表现良好,对于谱比法场地分类方法的改进和应用具有一定的参考价值。

Abstract:

The traditional spectral ratio method for seismic site classification often involves subjectivity and uncertainty. By integrating machine learning algorithms and leveraging large datasets along with feature extraction techniques,the objectivity and accuracy of the classification process can be enhanced,providing a more reliable foundation for seismic engineering design and hazard assessment. In this study,strong-motion waveform data from historical earthquakes in the Sichuan-Yunnan region were used to generate spectral ratio curves by calculating the horizontal and vertical velocity response spectrum values for various site categories. The characteristics of these curves,including predominant period,peak,and overall values,were analyzed. By incorporating deep learning algorithms,a dataset of spectral ratio curves from strong-motion stations in the Sichuan-Yunnan region was compiled and used as input for the VGG16 convolutional neural network(CNN)model. This model was trained to predict the probability distribution of three site categories based on NEHRP standards. The accuracy and generalization ability of the model were further enhanced through feature extraction and cross-validation techniques. The resulting site classification model demonstrated strong performance in validation,offering significant reference value for improving and applying the spectral ratio method in seismic site classification.