Geostatistical and network analysis of non-stationary spatial variation in ground motion amplitudes:
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| Format: | Elektronisch E-Book |
| Sprache: | English |
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Stanford, California
Stanford University
2021
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| Online-Zugang: | Volltext |
| Beschreibung: | Submitted to the Civil & Environmental Engineering Department |
| Beschreibung: | 1 Online-Ressource (147 Seiten) Illustrationen, Diagramme |
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| 505 | 8 | |a When an earthquake causes shaking in a region, the amplitude of shaking varies spatially. Ground motion models have been developed to predict the median and standard deviation of ground motion intensity measures. However, the remaining variation in ground motion prediction 'residuals' is significant, and shows spatial correlations at scales of tens of kilometers in separation distance. These correlations are important when assessing the risk to spatially distributed infrastructure or portfolios of properties. State of the art today is to assume that these spatial correlations depend mainly on separation distance (stationarity assumption). This dissertation aims to advance spatial correlation models of ground motions, by conducting a comprehensive correlation study on various data sets, evaluating key assumptions of current models, and proposing a novel framework for modeling spatial correlations. First, this dissertation proposes a method of site-specific correlation estimation and techniques for quantifying non-stationary spatial variations. Applying these methods to various data sets, factors related to non-stationary spatial correlations are investigated. Using physics-based ground motion simulations, it studies the dependency of non-stationary spatial correlations on source effects, path effects, and relative location to rupture. Using data from recent well-recorded earthquakes in New Zealand, it analyzes site-specific and region-specific correlations in ground motion amplitude for Wellington and Christchurch, and observed strong non-stationarity in spatial correlations. Results suggest that heterogeneous geologic conditions appear to be associated with the non-stationary spatial correlation. Second, this dissertation formulates a framework for detecting and modeling non-stationary correlations. By utilizing network analysis techniques, it proposes a community detection algorithm to find regions in spatial data with higher correlations. | |
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| contents | When an earthquake causes shaking in a region, the amplitude of shaking varies spatially. Ground motion models have been developed to predict the median and standard deviation of ground motion intensity measures. However, the remaining variation in ground motion prediction 'residuals' is significant, and shows spatial correlations at scales of tens of kilometers in separation distance. These correlations are important when assessing the risk to spatially distributed infrastructure or portfolios of properties. State of the art today is to assume that these spatial correlations depend mainly on separation distance (stationarity assumption). This dissertation aims to advance spatial correlation models of ground motions, by conducting a comprehensive correlation study on various data sets, evaluating key assumptions of current models, and proposing a novel framework for modeling spatial correlations. First, this dissertation proposes a method of site-specific correlation estimation and techniques for quantifying non-stationary spatial variations. Applying these methods to various data sets, factors related to non-stationary spatial correlations are investigated. Using physics-based ground motion simulations, it studies the dependency of non-stationary spatial correlations on source effects, path effects, and relative location to rupture. Using data from recent well-recorded earthquakes in New Zealand, it analyzes site-specific and region-specific correlations in ground motion amplitude for Wellington and Christchurch, and observed strong non-stationarity in spatial correlations. Results suggest that heterogeneous geologic conditions appear to be associated with the non-stationary spatial correlation. Second, this dissertation formulates a framework for detecting and modeling non-stationary correlations. By utilizing network analysis techniques, it proposes a community detection algorithm to find regions in spatial data with higher correlations. |
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Applying these methods to various data sets, factors related to non-stationary spatial correlations are investigated. Using physics-based ground motion simulations, it studies the dependency of non-stationary spatial correlations on source effects, path effects, and relative location to rupture. Using data from recent well-recorded earthquakes in New Zealand, it analyzes site-specific and region-specific correlations in ground motion amplitude for Wellington and Christchurch, and observed strong non-stationarity in spatial correlations. Results suggest that heterogeneous geologic conditions appear to be associated with the non-stationary spatial correlation. Second, this dissertation formulates a framework for detecting and modeling non-stationary correlations. 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| spelling | Chen, Yilin aut Geostatistical and network analysis of non-stationary spatial variation in ground motion amplitudes Stanford, California Stanford University 2021 1 Online-Ressource (147 Seiten) Illustrationen, Diagramme txt rdacontent c rdamedia cr rdacarrier Submitted to the Civil & Environmental Engineering Department When an earthquake causes shaking in a region, the amplitude of shaking varies spatially. Ground motion models have been developed to predict the median and standard deviation of ground motion intensity measures. However, the remaining variation in ground motion prediction 'residuals' is significant, and shows spatial correlations at scales of tens of kilometers in separation distance. These correlations are important when assessing the risk to spatially distributed infrastructure or portfolios of properties. State of the art today is to assume that these spatial correlations depend mainly on separation distance (stationarity assumption). This dissertation aims to advance spatial correlation models of ground motions, by conducting a comprehensive correlation study on various data sets, evaluating key assumptions of current models, and proposing a novel framework for modeling spatial correlations. First, this dissertation proposes a method of site-specific correlation estimation and techniques for quantifying non-stationary spatial variations. Applying these methods to various data sets, factors related to non-stationary spatial correlations are investigated. Using physics-based ground motion simulations, it studies the dependency of non-stationary spatial correlations on source effects, path effects, and relative location to rupture. Using data from recent well-recorded earthquakes in New Zealand, it analyzes site-specific and region-specific correlations in ground motion amplitude for Wellington and Christchurch, and observed strong non-stationarity in spatial correlations. Results suggest that heterogeneous geologic conditions appear to be associated with the non-stationary spatial correlation. Second, this dissertation formulates a framework for detecting and modeling non-stationary correlations. By utilizing network analysis techniques, it proposes a community detection algorithm to find regions in spatial data with higher correlations. Earthquake (DE-588)10275193-6 gnd rswk-swf (DE-588)4113937-9 Hochschulschrift 2021 gnd-content Earthquake (DE-588)10275193-6 b DE-604 Baker, Jack W. dgs http://purl.stanford.edu/yt513kn5953 Archivierung kostenfrei Volltext |
| spellingShingle | Chen, Yilin Geostatistical and network analysis of non-stationary spatial variation in ground motion amplitudes When an earthquake causes shaking in a region, the amplitude of shaking varies spatially. Ground motion models have been developed to predict the median and standard deviation of ground motion intensity measures. However, the remaining variation in ground motion prediction 'residuals' is significant, and shows spatial correlations at scales of tens of kilometers in separation distance. These correlations are important when assessing the risk to spatially distributed infrastructure or portfolios of properties. State of the art today is to assume that these spatial correlations depend mainly on separation distance (stationarity assumption). This dissertation aims to advance spatial correlation models of ground motions, by conducting a comprehensive correlation study on various data sets, evaluating key assumptions of current models, and proposing a novel framework for modeling spatial correlations. First, this dissertation proposes a method of site-specific correlation estimation and techniques for quantifying non-stationary spatial variations. Applying these methods to various data sets, factors related to non-stationary spatial correlations are investigated. Using physics-based ground motion simulations, it studies the dependency of non-stationary spatial correlations on source effects, path effects, and relative location to rupture. Using data from recent well-recorded earthquakes in New Zealand, it analyzes site-specific and region-specific correlations in ground motion amplitude for Wellington and Christchurch, and observed strong non-stationarity in spatial correlations. Results suggest that heterogeneous geologic conditions appear to be associated with the non-stationary spatial correlation. Second, this dissertation formulates a framework for detecting and modeling non-stationary correlations. By utilizing network analysis techniques, it proposes a community detection algorithm to find regions in spatial data with higher correlations. Earthquake (DE-588)10275193-6 gnd |
| subject_GND | (DE-588)10275193-6 (DE-588)4113937-9 |
| title | Geostatistical and network analysis of non-stationary spatial variation in ground motion amplitudes |
| title_auth | Geostatistical and network analysis of non-stationary spatial variation in ground motion amplitudes |
| title_exact_search | Geostatistical and network analysis of non-stationary spatial variation in ground motion amplitudes |
| title_exact_search_txtP | Geostatistical and network analysis of non-stationary spatial variation in ground motion amplitudes |
| title_full | Geostatistical and network analysis of non-stationary spatial variation in ground motion amplitudes |
| title_fullStr | Geostatistical and network analysis of non-stationary spatial variation in ground motion amplitudes |
| title_full_unstemmed | Geostatistical and network analysis of non-stationary spatial variation in ground motion amplitudes |
| title_short | Geostatistical and network analysis of non-stationary spatial variation in ground motion amplitudes |
| title_sort | geostatistical and network analysis of non stationary spatial variation in ground motion amplitudes |
| topic | Earthquake (DE-588)10275193-6 gnd |
| topic_facet | Earthquake Hochschulschrift 2021 |
| url | http://purl.stanford.edu/yt513kn5953 |
| work_keys_str_mv | AT chenyilin geostatisticalandnetworkanalysisofnonstationaryspatialvariationingroundmotionamplitudes AT bakerjackw geostatisticalandnetworkanalysisofnonstationaryspatialvariationingroundmotionamplitudes |