A Novel VS30 Prediction Strategy Taking Fluid Saturation into Account and a New VS30 Model of Türkiye

Abstract

The averaged shear-wave velocity of the top 30 m (VS30) is widely used in earthquake engineering as a proxy to represent site responses. However, the spatial availability of measured VS30 is rather limited, and, so far, a region-specific VS30 model that would aid prediction of strong ground motions is not yet developed for Türkiye. In this study, a new strategy for predicting VS30 is developed using data from Türkiye and California. At first, VS30 measurements are classified into four sedimentary classes according to their ages (Quaternary–Pliocene, Miocene, Paleogene, and Pre-Paleogene) and three nonsedimentary classes (Intrusive, Extrusive, and Metamorphic). Observations from Quaternary–Pliocene deposits are most abundant and characterized by large data scatter, thus further divided into two major landform groups. Because the reduction of VS with saturation is pronounced in soils due to capillary forces, Quaternary–Pliocene deposits are also differentiated as wet if the water table depth is less than 30 m and dry otherwise. In California, available groundwater measurements are utilized while flat areas with elevation differences less than 30 m from water bodies (sea, lake, and major rivers) are mapped out as wet zones throughout Türkiye. After the elimination of outliers, slope and elevation-based VS30 prediction equations are developed separately for subclasses of Quaternary–Pliocene, Miocene, and Paleogene-aged sedimentary units using multivariable linear regression, whereas VS30 values of Pre-Paleogene sedimentary and nonsedimentary units are fixed to the mean of each subclass. Resultant model misfits and comparisons with measurements from the microzonation study conducted across İstanbul clearly indicate that our proposed VS30 prediction strategy is performing better than the competing models tested, especially in the youngest sedimentary units, and thus provides a new, accurate VS30 model of Türkiye.