Regional seismic risk assessments and quantification of portfolio losses often require simulation of spatially distributed ground motions at multiple intensity measures. For a given earthquake, distributed ground motions are characterized by spatial correlation and correlation between different intensity measures, known as cross-correlation. This study proposes a new spatial cross-correlation model for within-event spectral acceleration residuals that uses a combination of principal component analysis (PCA) and geostatistics. Records from 45 earthquakes are used to investigate earthquake-to-earthquake trends in application of PCA to spectral acceleration residuals. Based on the findings, PCA is used to determine coefficients that linearly transform cross-correlated residuals to independent principal components. Nested semivariogram models are then fit to empirical semivariograms to quantify the spatial correlation of principal components. The resultant PCA spatial cross-correlation model is shown to be accurate and computationally efficient. A step-by-step procedure and an example are presented to illustrate the use of the predictive model for rapid simulation of spatially cross-correlated spectral accelerations at multiple periods.
- principal component analysis
- seismic risk
- spatial correlation
- spectral accelerations
ASJC Scopus subject areas
- Geotechnical Engineering and Engineering Geology
- Earth and Planetary Sciences (miscellaneous)