Spatially correlated Vs30 estimation in the Beijing area

Beijing is an international metropolis, that is also an earthquake-prone city. The aims of this study are detailed quantifying and qualifying soil layer properties for an accurate seismic safety evaluation in the Beijing area. The time average shear-wave velocity in the first 30 m of subsoil, V-s30, is an important site parameter used in site response analysis, site classification, and seismic loss estimation. Mapping of V-s30 over a city-scaled region is commonly done through proxy-based methods by correlating V-s30 with geological or topographic information. In this paper, a geostatistical-based random field model is presented and applied to mapping V-s30 over extended areas. This random field model is then coupled with Monte Carlo simulations to obtain an averaged V-s30 map and its associated uncertainties. Unlike the traditional deterministic prediction model, this framework accounts for spatial variations of V-s30 values and uncertainties, which makes the prediction more reliable. A total of 388 shear wave velocity measurements in the Beijing area are used to calculate V-s30 values, from which the statistical and spatial properties for the random field realizations are inferred. New spatially correlated probabilistic V-s30 maps for the Beijing area are then represented, and the effect of the maximum number of previously generated elements to correlate to in estimating V-s30 maps is tested.

Automated summary

This study aims to accurately evaluate the seismic safety of Beijing by quantifying and qualifying the soil layer properties. The time average shear-wave velocity in the first 30 m of subsoil, V-s30, is an important parameter used in seismic analysis and estimation. The paper presents a geostatistical-based random field model combined with Monte Carlo simulations to map V-s30 over extended areas, considering spatial variations and uncertainties. Using 388 shear wave velocity measurements in Beijing, probabilistic V-s30 maps are generated, testing the effect of the number of previously generated elements on the estimation.