Abstract
Shear wave velocity (Vs) is one of the most important parameters of a geological model to assess the site effect and the ground response. In this paper the spatial variability of shear wave velocity in Mashhad capital city are investigated. For this purpose, 243 Vs profiles of different projects throughout the city were used. Based on the Vs profiles the iso-level maps of the Vs interfaces 300, 500, 750, 950 and 1200 m/s were obtained by kriging interpolation method. The best semivariogram models were obtained with changing the effective parameters and assessing the components of the models and spatial dependence. The best models for the entire interfaces were exponential. Based on these models, the spatial dependence of depth data was moderate to strong. The performance of interpolations was checked by cross-validation and its indices i.e. mean standardized prediction errors (MSPR), root mean square prediction errors (RMSPE), average kriging standard error (AKSE), and root mean square standardized prediction errors (RMSSPE) were assessed. A trend of depth increasing towards the northeast was observed at all of the interfaces.
Highlights
A geological model including the geometry of the basin, the soil structure and the dynamic properties of the soil layers with great attention to the shear wave velocity (Vs) is a fundamental requirement for site effect studies [1]
The interfaces of 300, 500 and 750 m/s are located in shallow depths, as average positioned depth of these interfaces were 4, 13, and 25 meters, respectively that could be attributed to the stiff soils and arid climate of the Mashhad city
It is found that ordinary kriging is the best estimator to find the best fitted semivariogram to depth data
Summary
A geological model including the geometry of the basin, the soil structure and the dynamic properties of the soil layers with great attention to the shear wave velocity (Vs) is a fundamental requirement for site effect studies [1]. Many studies have been performed around the world to assess site effect using different methods of 1D, 2D or. There are different geostatistical techniques such as kriging, inverse distance weighting (IDW), splines and triangulation which convert the point data to continuous surfaces by using the spatial interpolation. Most of these methods assume that the adjacent samples tend to be more similar than those which are further apart. Kriging is the best linear unbiased estimator which may be free from systematic error and have minimum variance [9]-[11]
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