Sayed S. R. Moustafa, Ahmad M. Faried, Mohamed H. Yassien
{"title":"利用遗传优化方法反演埃及国家地震台网站点微震数据","authors":"Sayed S. R. Moustafa, Ahmad M. Faried, Mohamed H. Yassien","doi":"10.1007/s10950-024-10245-0","DOIUrl":null,"url":null,"abstract":"<div><p>Precise site response characterization is essential for understanding lithostratigraphic subsurface properties, seismic site effects, and soil classification within seismic networks. This study addresses the challenge of limited shear-wave velocity data within the upper 30 meters (<span>\\(V_{S30}\\)</span>) at 39 stations of the Egyptian National Seismic Network (ENSN) in Northern Egypt. We employ the inversion of Horizontal-to-Vertical Spectral Ratio (HVSR) data from single-station ambient noise using an Elitist Genetic Algorithm (EGA) to estimate the shear-wave velocity profile at each station. This algorithm uses an equivalent linear approach based on the viscoelastic Kelvin-Voigt model to compute the theoretical site response of horizontally stratified soil layers. Inversion results from Multichannel Analysis of Surface Waves (MASW) conducted at five ENSN stations were incorporated to refine the input inversion parameters and control the genetic HVSR inversion outcomes. This approach effectively demonstrates the HVSR method’s ability to detect variations in the shear-wave velocity structure with depth and determine the average shear-wave velocity in the upper 30 meters. The obtained site-specific amplification data contributes to a more detailed understanding of site conditions, enabling precise determination of site classification and characterization factors. This facilitates refined Peak Ground Acceleration (PGA) estimations, thereby substantially enhancing the robustness of future seismic hazard assessments in Egypt.</p></div>","PeriodicalId":16994,"journal":{"name":"Journal of Seismology","volume":"28 6","pages":"1445 - 1470"},"PeriodicalIF":1.6000,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Characterizing Egyptian National Seismic Network station sites using genetic optimization for microtremor data inversion\",\"authors\":\"Sayed S. R. Moustafa, Ahmad M. Faried, Mohamed H. Yassien\",\"doi\":\"10.1007/s10950-024-10245-0\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Precise site response characterization is essential for understanding lithostratigraphic subsurface properties, seismic site effects, and soil classification within seismic networks. This study addresses the challenge of limited shear-wave velocity data within the upper 30 meters (<span>\\\\(V_{S30}\\\\)</span>) at 39 stations of the Egyptian National Seismic Network (ENSN) in Northern Egypt. We employ the inversion of Horizontal-to-Vertical Spectral Ratio (HVSR) data from single-station ambient noise using an Elitist Genetic Algorithm (EGA) to estimate the shear-wave velocity profile at each station. This algorithm uses an equivalent linear approach based on the viscoelastic Kelvin-Voigt model to compute the theoretical site response of horizontally stratified soil layers. Inversion results from Multichannel Analysis of Surface Waves (MASW) conducted at five ENSN stations were incorporated to refine the input inversion parameters and control the genetic HVSR inversion outcomes. This approach effectively demonstrates the HVSR method’s ability to detect variations in the shear-wave velocity structure with depth and determine the average shear-wave velocity in the upper 30 meters. The obtained site-specific amplification data contributes to a more detailed understanding of site conditions, enabling precise determination of site classification and characterization factors. This facilitates refined Peak Ground Acceleration (PGA) estimations, thereby substantially enhancing the robustness of future seismic hazard assessments in Egypt.</p></div>\",\"PeriodicalId\":16994,\"journal\":{\"name\":\"Journal of Seismology\",\"volume\":\"28 6\",\"pages\":\"1445 - 1470\"},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2024-10-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Seismology\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s10950-024-10245-0\",\"RegionNum\":4,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"GEOCHEMISTRY & GEOPHYSICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Seismology","FirstCategoryId":"89","ListUrlMain":"https://link.springer.com/article/10.1007/s10950-024-10245-0","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"GEOCHEMISTRY & GEOPHYSICS","Score":null,"Total":0}
引用次数: 0
摘要
精确的现场反应表征对于了解地震台网内的岩石地层地下性质、地震现场效应和土壤分类至关重要。本研究解决了埃及北部国家地震台网(ENSN) 39个台站30米(\(V_{S30}\))内有限的横波速度数据的挑战。我们利用精英遗传算法(EGA)反演单站环境噪声的水平-垂直谱比(HVSR)数据,估计每个站点的横波速度剖面。该算法采用基于黏弹性Kelvin-Voigt模型的等效线性方法计算水平分层土层的理论场地响应。利用5个ENSN台站的多通道表面波分析(Multichannel Analysis of Surface Waves, MASW)反演结果,优化输入反演参数,控制HVSR反演结果。该方法有效地证明了HVSR方法能够探测剪切波速度结构随深度的变化,并确定上部30米的平均剪切波速度。获得的位点特异性扩增数据有助于更详细地了解位点条件,从而精确确定位点分类和表征因素。这有助于精确估计峰值地面加速度(PGA),从而大大提高埃及未来地震灾害评估的稳健性。
Characterizing Egyptian National Seismic Network station sites using genetic optimization for microtremor data inversion
Precise site response characterization is essential for understanding lithostratigraphic subsurface properties, seismic site effects, and soil classification within seismic networks. This study addresses the challenge of limited shear-wave velocity data within the upper 30 meters (\(V_{S30}\)) at 39 stations of the Egyptian National Seismic Network (ENSN) in Northern Egypt. We employ the inversion of Horizontal-to-Vertical Spectral Ratio (HVSR) data from single-station ambient noise using an Elitist Genetic Algorithm (EGA) to estimate the shear-wave velocity profile at each station. This algorithm uses an equivalent linear approach based on the viscoelastic Kelvin-Voigt model to compute the theoretical site response of horizontally stratified soil layers. Inversion results from Multichannel Analysis of Surface Waves (MASW) conducted at five ENSN stations were incorporated to refine the input inversion parameters and control the genetic HVSR inversion outcomes. This approach effectively demonstrates the HVSR method’s ability to detect variations in the shear-wave velocity structure with depth and determine the average shear-wave velocity in the upper 30 meters. The obtained site-specific amplification data contributes to a more detailed understanding of site conditions, enabling precise determination of site classification and characterization factors. This facilitates refined Peak Ground Acceleration (PGA) estimations, thereby substantially enhancing the robustness of future seismic hazard assessments in Egypt.
期刊介绍:
Journal of Seismology is an international journal specialising in all observational and theoretical aspects related to earthquake occurrence.
Research topics may cover: seismotectonics, seismicity, historical seismicity, seismic source physics, strong ground motion studies, seismic hazard or risk, engineering seismology, physics of fault systems, triggered and induced seismicity, mining seismology, volcano seismology, earthquake prediction, structural investigations ranging from local to regional and global studies with a particular focus on passive experiments.