{"title":"欧洲浅层地壳地震地动参数的非参数模型","authors":"","doi":"10.1016/j.soildyn.2024.108923","DOIUrl":null,"url":null,"abstract":"<div><p>The current study focuses on deriving ground motion models (GMMs) for 21 ground motion parameters derived from data sourced from the Engineering Strong Motion (ESM) database. These parameters include Peak Ground Acceleration (PGA), Peak Ground Velocity (PGV), Peak Ground Displacement (PGD), PGV-to-PGA ratio, (V/H) PGA ratio Predominant Frequency (<span><math><mrow><msub><mi>F</mi><mi>p</mi></msub></mrow></math></span>), Central Frequency (<span><math><mrow><mi>Ω</mi></mrow></math></span>), Spectral Parameter (<span><math><mrow><mi>q</mi></mrow></math></span>), Significant Duration (<span><math><mrow><msub><mi>T</mi><mrow><mi>S</mi><mi>i</mi><mi>g</mi></mrow></msub></mrow></math></span>), Root Mean Square Acceleration (<span><math><mrow><msub><mi>A</mi><mrow><mi>r</mi><mi>m</mi><mi>s</mi></mrow></msub></mrow></math></span>), Arias Intensity (<span><math><mrow><msub><mi>I</mi><mi>a</mi></msub></mrow></math></span>), Cumulative Absolute Velocity (CAV), Characteristic Intensity (<span><math><mrow><msub><mi>I</mi><mi>C</mi></msub></mrow></math></span>), Acceleration Spectrum Intensity (ASI), Velocity Spectrum Intensity (VSI), Total Energy (<span><math><mrow><msub><mi>E</mi><mrow><mi>a</mi><mi>c</mi><mi>c</mi></mrow></msub></mrow></math></span>), Spectral Centroid (<span><math><mrow><msub><mi>E</mi><mi>w</mi></msub></mrow></math></span>), Spectral Standard Deviation (<span><math><mrow><msub><mi>S</mi><mi>w</mi></msub></mrow></math></span>), Temporal Centroid (<span><math><mrow><msub><mi>E</mi><mi>t</mi></msub></mrow></math></span>), Temporal Standard Deviation (<span><math><mrow><msub><mi>S</mi><mi>t</mi></msub></mrow></math></span>), and Correlation between time and frequency [<span><math><mrow><mi>ρ</mi><mrow><mo>(</mo><mrow><mi>t</mi><mo>,</mo><mi>ω</mi></mrow><mo>)</mo></mrow></mrow></math></span>]. Both horizontal and vertical components are considered in this study. The inherent random effects within ground motion regression, encompassing inter-event, inter-site, inter-locality, and inter-region variabilities, are addressed using cross-nested mixed effect regression utilizing a non-parametric GMM approach employing Artificial Neural Network (ANN). Quantitative assessment of the models involves correlation coefficients for regression through the origin and error measures like mean squared error and mean absolute error. These findings of the assessment confirm reliable estimates of Ground Motion Parameters (GMPs). A comparison of GMPs computed using the proposed model and those reported in the literature indicated model's superior performance. Furthermore, satisfactory performance of the proposed GMM in ground motion simulation for the ESM region is demonstrated.</p></div>","PeriodicalId":49502,"journal":{"name":"Soil Dynamics and Earthquake Engineering","volume":null,"pages":null},"PeriodicalIF":4.2000,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A non-parametric model of ground motion parameters for shallow crustal earthquakes in Europe\",\"authors\":\"\",\"doi\":\"10.1016/j.soildyn.2024.108923\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The current study focuses on deriving ground motion models (GMMs) for 21 ground motion parameters derived from data sourced from the Engineering Strong Motion (ESM) database. These parameters include Peak Ground Acceleration (PGA), Peak Ground Velocity (PGV), Peak Ground Displacement (PGD), PGV-to-PGA ratio, (V/H) PGA ratio Predominant Frequency (<span><math><mrow><msub><mi>F</mi><mi>p</mi></msub></mrow></math></span>), Central Frequency (<span><math><mrow><mi>Ω</mi></mrow></math></span>), Spectral Parameter (<span><math><mrow><mi>q</mi></mrow></math></span>), Significant Duration (<span><math><mrow><msub><mi>T</mi><mrow><mi>S</mi><mi>i</mi><mi>g</mi></mrow></msub></mrow></math></span>), Root Mean Square Acceleration (<span><math><mrow><msub><mi>A</mi><mrow><mi>r</mi><mi>m</mi><mi>s</mi></mrow></msub></mrow></math></span>), Arias Intensity (<span><math><mrow><msub><mi>I</mi><mi>a</mi></msub></mrow></math></span>), Cumulative Absolute Velocity (CAV), Characteristic Intensity (<span><math><mrow><msub><mi>I</mi><mi>C</mi></msub></mrow></math></span>), Acceleration Spectrum Intensity (ASI), Velocity Spectrum Intensity (VSI), Total Energy (<span><math><mrow><msub><mi>E</mi><mrow><mi>a</mi><mi>c</mi><mi>c</mi></mrow></msub></mrow></math></span>), Spectral Centroid (<span><math><mrow><msub><mi>E</mi><mi>w</mi></msub></mrow></math></span>), Spectral Standard Deviation (<span><math><mrow><msub><mi>S</mi><mi>w</mi></msub></mrow></math></span>), Temporal Centroid (<span><math><mrow><msub><mi>E</mi><mi>t</mi></msub></mrow></math></span>), Temporal Standard Deviation (<span><math><mrow><msub><mi>S</mi><mi>t</mi></msub></mrow></math></span>), and Correlation between time and frequency [<span><math><mrow><mi>ρ</mi><mrow><mo>(</mo><mrow><mi>t</mi><mo>,</mo><mi>ω</mi></mrow><mo>)</mo></mrow></mrow></math></span>]. Both horizontal and vertical components are considered in this study. The inherent random effects within ground motion regression, encompassing inter-event, inter-site, inter-locality, and inter-region variabilities, are addressed using cross-nested mixed effect regression utilizing a non-parametric GMM approach employing Artificial Neural Network (ANN). Quantitative assessment of the models involves correlation coefficients for regression through the origin and error measures like mean squared error and mean absolute error. These findings of the assessment confirm reliable estimates of Ground Motion Parameters (GMPs). A comparison of GMPs computed using the proposed model and those reported in the literature indicated model's superior performance. Furthermore, satisfactory performance of the proposed GMM in ground motion simulation for the ESM region is demonstrated.</p></div>\",\"PeriodicalId\":49502,\"journal\":{\"name\":\"Soil Dynamics and Earthquake Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.2000,\"publicationDate\":\"2024-08-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Soil Dynamics and Earthquake Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0267726124004755\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, GEOLOGICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Soil Dynamics and Earthquake Engineering","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0267726124004755","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, GEOLOGICAL","Score":null,"Total":0}
A non-parametric model of ground motion parameters for shallow crustal earthquakes in Europe
The current study focuses on deriving ground motion models (GMMs) for 21 ground motion parameters derived from data sourced from the Engineering Strong Motion (ESM) database. These parameters include Peak Ground Acceleration (PGA), Peak Ground Velocity (PGV), Peak Ground Displacement (PGD), PGV-to-PGA ratio, (V/H) PGA ratio Predominant Frequency (), Central Frequency (), Spectral Parameter (), Significant Duration (), Root Mean Square Acceleration (), Arias Intensity (), Cumulative Absolute Velocity (CAV), Characteristic Intensity (), Acceleration Spectrum Intensity (ASI), Velocity Spectrum Intensity (VSI), Total Energy (), Spectral Centroid (), Spectral Standard Deviation (), Temporal Centroid (), Temporal Standard Deviation (), and Correlation between time and frequency []. Both horizontal and vertical components are considered in this study. The inherent random effects within ground motion regression, encompassing inter-event, inter-site, inter-locality, and inter-region variabilities, are addressed using cross-nested mixed effect regression utilizing a non-parametric GMM approach employing Artificial Neural Network (ANN). Quantitative assessment of the models involves correlation coefficients for regression through the origin and error measures like mean squared error and mean absolute error. These findings of the assessment confirm reliable estimates of Ground Motion Parameters (GMPs). A comparison of GMPs computed using the proposed model and those reported in the literature indicated model's superior performance. Furthermore, satisfactory performance of the proposed GMM in ground motion simulation for the ESM region is demonstrated.
期刊介绍:
The journal aims to encourage and enhance the role of mechanics and other disciplines as they relate to earthquake engineering by providing opportunities for the publication of the work of applied mathematicians, engineers and other applied scientists involved in solving problems closely related to the field of earthquake engineering and geotechnical earthquake engineering.
Emphasis is placed on new concepts and techniques, but case histories will also be published if they enhance the presentation and understanding of new technical concepts.