In the fields of engineering seismology and earthquake engineering, researchers have predominantly focused on ground motion models (GMMs) for intensity measures. However, there has been limited research on power spectral density GMMs (PSD‐GMMs) that characterize spectral characteristics. PSD, being structure‐independent, offers unique advantages. This study aims to construct PSD‐GMMs using non‐parametric machine learning (ML) techniques. By considering 241 different frequencies from 0.1 to 25.12 Hz and evaluating eight performance indicators, seven highly accurate and stable ML techniques are selected from 12 different ML techniques as foundational models for the PSD‐GMM. Through mixed effects regression analysis, inter‐event, intra‐event, and inter‐site standard deviations are derived. To address inherent modeling uncertainty, this study uses the ratio of the reciprocal of the standard deviation of the total residuals of the foundational models to the sum of the reciprocals of the total residuals of the seven ML GMMs as weight coefficients for constructing a hybrid non‐parametric PSD‐GMM. Utilizing this model, ground motion records can be simulated, and seismic hazard curves and uniform hazard PSD can be obtained. In summary, the hybrid non‐parametric PSD‐GMM demonstrates remarkable efficacy in simulating and predicting ground motion records and holds significant potential for guiding seismic hazard and risk analysis.

中文翻译：

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基于机器学习的功率谱密度混合非参数地震动模型


在工程地震学和地震工程领域，研究人员主要关注用于强度测量的地面运动模型（GMM）。然而，对于表征频谱特性的功率谱密度 GMM（PSD-GMM）的研究还很有限。 PSD 与结构无关，具有独特的优势。本研究旨在使用非参数机器学习 (ML) 技术构建 PSD-GMM。通过考虑从 0.1 到 25.12 Hz 的 241 个不同频率并评估 8 个性能指标，从 12 种不同的 ML 技术中选择了 7 种高精度和稳定的 ML 技术作为 PSD-GMM 的基础模型。通过混合效应回归分析，得出事件间、事件内和站点间标准差。为了解决固有的建模不确定性，本研究使用基础模型总残差的标准差的倒数与七个 ML GMM 的总残差的倒数之和的比率作为权重系数，用于构建混合非参数 PSD-GMM。利用该模型可以模拟地震动记录，得到地震危险曲线和统一危险PSD。总之，混合非参数PSD-GMM在模拟和预测地震动记录方面表现出显着的功效，并且在指导地震灾害和风险分析方面具有巨大的潜力。