Spatial correlation assessment of multiple earthquake intensity measures using physics-based simulated ground motions

Abstract

Predictive models for spatial correlation play an effective role in the assessment of seismic risk associated with distributed infrastructure and building portfolios. However, existing models often rely on simplified approaches, assuming isotropy and stationarity. This paper verifies these assumptions by presenting a comprehensive study using a database of 3D physics-based simulated broadband ground motions for Istanbul, generated by the SPEED software. The results reveal significant event-to-event variability and nonstationary and anisotropic characteristics of spatial correlation influenced by source, path, and site effects. The development of nonstationary correlation models requires exploring influential metrics beyond spatial proximity and gaining a deep understanding of their impact, which is the focus of this study. Analysis of the spatial correlations of peak ground displacement, peak ground velocity, peak ground acceleration, and response spectral accelerations at different periods, employing both stationary and nonstationary correlation modelling methods and considering the finite fault model, indicates that the slip distribution pattern, direction and distance of station pairs relative to earthquake rupture, soil softness, and homogeneity of soil properties significantly influence the spatial correlations of near-field earthquake ground motions. Implementation of the introduced parameters in predictive spatial correlation models enhances the precision of regional seismic hazard assessments.

Keywords: 3D physics-based numerical simulation; Near-source ground motion; Regional seismic hazard; Spatial correlation.

Fig. 1

(a) The stations (shaded area is reperesenting of dense network of close stations) and epicenters (stars) of the simulated earthquake scenarios used in this study; (b) the Vs30 map of the shaollow soil of the case study. These maps were created using QGIS software (version 3.12.2) available at https://qgis.org.

Fig. 2

The mean and standard deviation of normalized within-event residuals vursus distance to rupture, $V_{s30}$ and $M_w$ for: (a) Sa(T = 1 s); (b) PGD; (c) PGV.

Fig. 3

(a) The mean and standard deviation of correlation ranges for PGD, PGV, PGA and PSA with periods of 0.1 s to 5 s. (b) The coefficient of variation of correlation ranges over 65 earthquake scenarios.

Fig. 4

Smoothed variogram surface for within-event residuals of (a) PGD, Mw = 7, Scenario No. 3 (b) PGV, Mw = 7.2, Scenario No. 2. (c) Sa(T = 1 s), Mw = 7, Scenario No. 1

Fig. 5

(a) HB94 slip distribution model. (b) CA15 slip distribution model (c) the mean and deviation of correlation range for PGD, PGV, PGA, and PSA of earthquake scenarios classified by magnitude (d) the mean and deviation of the correlation range for PGD, PGV, PGA, and PSA of earthquake scenarios classified by source model and magnitude.

Fig. 6

Comparison of spatial correlation models from this study for Sa(T = 1 s) with existing emperical models.

Fig. 7

(a) Spatial correlation models of station pairs with various mean distances to the rupture using Mw = 7.4 scenarios for within-event residuals of Sa(T = 3 s); (b) the coefficient of variation of correlation ranges regarding the variation of station pairs distance from the rupture.

Fig. 8

The mean correlation coefficients versus the azimuth of separation distance vector using Mw = 7.4 scenarios for within-event residuals of: (a) Sa(T = 3 s); (b) Sa(T = 1 s); (c) PGA; (d) PGV; (e) PGD; (f) the mean separation distances of station pairs associated with different direction bins.

Fig. 9

(a) The spatial correlation models for the within-event residuals of Sa(T = 1 s) computed for station pairs in various directions as a function of separation distance using Mw = 7.4 scenarios; (b) the coefficient of variation of correlation ranges for different IMs using Mw = 7.4 scenarios.

Fig. 10

Spatial correlation as a function of the differnces of the soil properties of station pairs.

Fig. 11

Spatial correlation as a function of shear wave velocity of the soil of station pairs.