使用支持向量机的场地分类方法:一项研究

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引用次数: 0

摘要

在分析地震风险和建立地动衰减关系时,场地效应是一个关键因素。一些国家在抗震设计规范中引入了建筑场地分类,以考虑当地场地对地面运动的影响。然而,大多数场地分类指标都依赖于钻探数据,而钻探数据通常价格昂贵,且需要大量人力。因此,较不详细的钻探数据可能会导致众多台站的场地类别无法确定。本研究利用 KiK 网和 K 网的强地动数据和站点数据,训练了基于支持向量机(SVM)算法的站点分类模型,以解决这一问题。分类模型使用了标注地点的平均 HVSR 曲线以及从曲线中提取的频率、峰值、"突出度 "和 "尖锐度 "等综合输入。SVM 分类模型在测试集中的准确率为 76.12%,对站点 I、II 和 III 的召回率分别为 82.69%、75% 和 63.64%。精确率分别为 75.44%、73.77% 和 87.50%,F1 分数分别为 78.90%、74.38% 和 73.68%。对于 HVSR 曲线上没有明显峰值的站点,则使用 HVSR 曲线值作为特征参数(输入),同时训练基于 SVM 的站点分类模型。I 类和 II 类的准确率为 75.86%。研究结果表明,与光谱比曲线匹配法和 GRNN 法相比,召回率和准确率更高,表明分类性能更好。最后,利用 "国家地震烈度速报与预警工程 "在新疆部署的一些基本台站验证了该模型的泛化能力。基于 SVM 的场地分类模型采用了强震运动数据,可以为没有详细钻孔资料的场地提供更可靠的分类结果,其场地分类结果可作为探测地动衰减关系、地动模拟和考虑场地效应的抗震设防的参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Site classification methodology using support vector machine: A study
The site effect is a crucial factor when analyzing seismic risk and establishing ground motion attenuation relationships. A number of countries have introduced building site classification into earthquake-resistant design codes to account for local site effects on ground motion. However, most site classification indicators rely on drilling data, which is often expensive and requires considerable manpower. As a result, the less detailed drilling data may lead to an undetermined site category of numerous stations. In this study, a Support Vector Machine (SVM) algorithm-based site classification model was trained to address this issue using strong ground motion data and site data from KiK-net and K-net. The classification model used the average HVSR curve of the labeled site and the combined inputs, including frequency, peak, “prominence, and “sharpness” extracted from the curve. The SVM classification model has an accuracy of 76.12% on the test set, with recall rates of 82.69%, 75%, and 63.64% for sites I, II, and III, respectively. The precision rates are 75.44%, 73.77%, and 87.50%, respectively, with F1 scores of 78.90%, 74.38%, and 73.68%. For sites without significant peaks in the HVSR curve, the HVSR curve value was used as the characteristic parameter (input), and the SVM-based site classification model was also trained. The accuracy of class I and II is 75.86%. The results of this study show higher recall and accuracy rates than those obtained using the spectral ratio curve matching method and GRNN method, indicating a better classification performance. Finally, the generalization ability of the model was verified using some basic stations in Xinjiang deployed by the “National Seismic Intensity Rapid Reporting and Early Warning Project”. The SVM-based site classification model that employs strong motion data can provide more reliable classification results for sites without detailed borehole information, and the site classification results can serve as a reference for probing ground motion attenuation relationships, ground motion simulation, and seismic fortification considering the site effect.
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