{"title":"结合相关性和一致性的新型地动强度空间变化模型","authors":"Pan Wen, Baofeng Zhou, Guoliang Shao","doi":"10.1785/0220230249","DOIUrl":null,"url":null,"abstract":"\n Regional seismic risk or loss assessments generally require simulation of spatially distributed ground motions using multiple intensity measures. Hence, in this study, ground-motion model estimation is performed with a spatial correlation. Previously, many researchers have analyzed spatial correlations and developed empirical models using ground-motion recordings. In this study, ground motions occurring in California between 2019 and 2023 were used to analyze spatial correlations using semivariograms for the peak ground acceleration and pseudospectral acceleration in various spectral periods. Based on the analysis results, two aspects need to be revised in the conventional correlation model: (1) the empirical exponential model cannot reasonably reflect the target spatial correlation at a separation distance <10 km, and (2) the variation in the spatial correlation ground-motion intensity cannot be described at a small separation distance <1 km. Owing to these limitations, we revised the fitting model of the semivariogram to better characterize the spatial correlation. In the model, another function called coherency, replaced the spatial correlation to characterize the variation in the Fourier phase rather than the intensity within a separation distance <1 km. This research shows that the spatial variation in any region can be analyzed by combining the coherence and correlation functions for practical seismic-risk or loss assessment problems.","PeriodicalId":508466,"journal":{"name":"Seismological Research Letters","volume":" 6","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A New Spatial Variation Model for Ground-Motion Intensities Combined with Correlation and Coherency\",\"authors\":\"Pan Wen, Baofeng Zhou, Guoliang Shao\",\"doi\":\"10.1785/0220230249\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n Regional seismic risk or loss assessments generally require simulation of spatially distributed ground motions using multiple intensity measures. Hence, in this study, ground-motion model estimation is performed with a spatial correlation. Previously, many researchers have analyzed spatial correlations and developed empirical models using ground-motion recordings. In this study, ground motions occurring in California between 2019 and 2023 were used to analyze spatial correlations using semivariograms for the peak ground acceleration and pseudospectral acceleration in various spectral periods. Based on the analysis results, two aspects need to be revised in the conventional correlation model: (1) the empirical exponential model cannot reasonably reflect the target spatial correlation at a separation distance <10 km, and (2) the variation in the spatial correlation ground-motion intensity cannot be described at a small separation distance <1 km. Owing to these limitations, we revised the fitting model of the semivariogram to better characterize the spatial correlation. In the model, another function called coherency, replaced the spatial correlation to characterize the variation in the Fourier phase rather than the intensity within a separation distance <1 km. This research shows that the spatial variation in any region can be analyzed by combining the coherence and correlation functions for practical seismic-risk or loss assessment problems.\",\"PeriodicalId\":508466,\"journal\":{\"name\":\"Seismological Research Letters\",\"volume\":\" 6\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-03-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Seismological Research Letters\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1785/0220230249\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Seismological Research Letters","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1785/0220230249","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
区域地震风险或损失评估通常需要使用多种烈度测量方法对空间分布的地动进行模拟。因此,在本研究中,利用空间相关性进行地动模型估算。此前,许多研究人员利用地动记录分析了空间相关性并开发了经验模型。在本研究中,利用 2019 年至 2023 年期间发生在加利福尼亚州的地面运动,使用不同频谱周期的峰值地面加速度和伪谱加速度的半变量图分析空间相关性。根据分析结果,传统的相关模型有两个方面需要修改:(1)经验指数模型不能合理地反映距离小于 10 km 的目标空间相关性;(2)空间相关地动强度的变化不能在距离小于 1 km 时得到描述。由于这些局限性,我们修改了半变量图的拟合模型,以更好地描述空间相关性。在该模型中,另一个名为 "一致性 "的函数取代了空间相关性,以描述傅里叶相位的变化,而不是距离小于 1 千米范围内的强度变化。这项研究表明,在实际的地震风险或损失评估问题中,可以通过结合相干性和相关性函数来分析任何区域的空间变化。
A New Spatial Variation Model for Ground-Motion Intensities Combined with Correlation and Coherency
Regional seismic risk or loss assessments generally require simulation of spatially distributed ground motions using multiple intensity measures. Hence, in this study, ground-motion model estimation is performed with a spatial correlation. Previously, many researchers have analyzed spatial correlations and developed empirical models using ground-motion recordings. In this study, ground motions occurring in California between 2019 and 2023 were used to analyze spatial correlations using semivariograms for the peak ground acceleration and pseudospectral acceleration in various spectral periods. Based on the analysis results, two aspects need to be revised in the conventional correlation model: (1) the empirical exponential model cannot reasonably reflect the target spatial correlation at a separation distance <10 km, and (2) the variation in the spatial correlation ground-motion intensity cannot be described at a small separation distance <1 km. Owing to these limitations, we revised the fitting model of the semivariogram to better characterize the spatial correlation. In the model, another function called coherency, replaced the spatial correlation to characterize the variation in the Fourier phase rather than the intensity within a separation distance <1 km. This research shows that the spatial variation in any region can be analyzed by combining the coherence and correlation functions for practical seismic-risk or loss assessment problems.