This study assesses the spatial correlation of broadband earthquake ground motions from 3D physics-based numerical simulations in near-source conditions. State-of-the-art models for predicting the spatial correlation are derived from wide datasets including densely recorded earthquakes in different areas worldwide and, therefore, they may be poorly representative of specific regions and near-source effects. A large set of broadband ground motions simulated by the SPEED code, and enriched in the high-frequency range with an Artificial Neural Network technique, is used to investigate the sensitivity of crucial parameters in geostatistical analysis (number of receivers), as well as of source, path, and site effects on spatial correlation, with a level of detail which could not be possible otherwise due to the paucity of recordings. First of all, the comparison of our results with those derived from earthquake recordings validates successfully the numerical approach in predicting the spatial correlation in a broad frequency range. Furthermore, the study points out that spatial correlation of response spectral accelerations is significantly affected by the magnitude, forward directivity effects, ground-motion directionality (fault normal versus fault parallel), and relative position from the causative fault. These features may make critical the use of isotropic and stationary models especially in near-fault conditions.
Spatial correlation of broadband ground motions from physics-based numerical simulations
Maria Infantino;Chiara Smerzini;Jiayue Lin
2021-01-01
Abstract
This study assesses the spatial correlation of broadband earthquake ground motions from 3D physics-based numerical simulations in near-source conditions. State-of-the-art models for predicting the spatial correlation are derived from wide datasets including densely recorded earthquakes in different areas worldwide and, therefore, they may be poorly representative of specific regions and near-source effects. A large set of broadband ground motions simulated by the SPEED code, and enriched in the high-frequency range with an Artificial Neural Network technique, is used to investigate the sensitivity of crucial parameters in geostatistical analysis (number of receivers), as well as of source, path, and site effects on spatial correlation, with a level of detail which could not be possible otherwise due to the paucity of recordings. First of all, the comparison of our results with those derived from earthquake recordings validates successfully the numerical approach in predicting the spatial correlation in a broad frequency range. Furthermore, the study points out that spatial correlation of response spectral accelerations is significantly affected by the magnitude, forward directivity effects, ground-motion directionality (fault normal versus fault parallel), and relative position from the causative fault. These features may make critical the use of isotropic and stationary models especially in near-fault conditions.File | Dimensione | Formato | |
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