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首页> 外文期刊>Seismological research letters >Support Vector Regression for Developing Ground‐Motion Models for Arias Intensity, Cumulative Absolute Velocity, and Significant Duration for the Kanto Region, Japan
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Support Vector Regression for Developing Ground‐Motion Models for Arias Intensity, Cumulative Absolute Velocity, and Significant Duration for the Kanto Region, Japan

机译：Support Vector Regression for Developing Ground‐Motion Models for Arias Intensity, Cumulative Absolute Velocity, and Significant Duration for the Kanto Region, Japan

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摘要

The Kanto region is an earthquake disaster‐prone area where it is necessary to conduct regional seismic hazard analysis. Ground‐motion models (GMMs) of Arias intensity, cumulative absolute velocity, and significant duration are developed by support vector regression (SVR) for the Kanto region, Japan. In contrast to traditional regression programs used in previous models, which are usually expressed as a mathematical function with a minimum observed training error as constraints, the SVR algorithm has one major feature: it minimizes the generalized error bound to improve robustness. In the database for this study, the regional ground‐motion database contains 15,960 ground‐motion records of 130 earthquake events from 2000 to 2020 with the Japan Meteorological Agency (JMA) with a magnitude MJMA 5.0–8.0 and a rupture distance less than 200 km. In developing SVR GMMs, the moment magnitude (⁠Mw⁠), rupture distance (⁠Rrup⁠), and shear‐wave velocity averaged in the top 30 m of soil (⁠VS30⁠) were adopted to characterize the source, path, and site conditions. To verify the rationality and effectiveness of the SVR GMMs, the performance indices (e.g., correlation coefficients and slope coefficients) and residuals are analyzed. The residuals of the SVR GMMs have no significant deviation in magnitude, rupture distance, or VS30⁠. The standard deviations of model residuals are calculated using the regional ground‐motion database, and the standard deviations of SVR GMMs are less than those of previous models developed based on a Japanese or global database. Furthermore, the SVR GMMs are also compared with observed data and the previous GMMs. Data‐driven SVR method constrains statistical theory and probability theory to develop GMMs, which can eliminate the problem that the specific form of the previous models may affect the prediction performance and capture the regional attenuation characteristics effectively.

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