The SAFER geodatabase for the Kathmandu valley: Bayesian kriging for data-scarce regions

Abstract

Geostatistical methods are valuable to better understand the spatial distribution of geotechnical parameters at regional scale and to optimize the locations of future ground investigations. This article investigates the use of the kriging interpolation method to extend the knowledge of a specific geotechnical property from a few sites to a broader geographical area with a focus on the Kathmandu valley (Nepal). A Bayesian form of kriging is proposed in this article. The estimation of the shear wave velocity in the uppermost 30 m of soil ( VS30) in the Kathmandu valley is examined. Slope-based VS30 estimates from the United States Geological Survey are used as prior information, and 15 VS30 measurements are used as more precise data. Considering the limited number of high-quality VS30 measurements available in the valley, it is shown that the Bayesian scheme can lead to a more robust estimation of VS30 than that obtained with the ordinary kriging approach. A methodology for conditioning prior low-precision data to the measurements is also presented.