A best linear unbiased estimator for interpolation at unsampled locations based on available observations. All estimates at unsampled locations are assumed as linear combination of observations assigned with different weighting factors.
Published in Chapter:
Spatial Variability Analysis of Soil Properties using Geostatistics
Anand J. Puppala (The University of Texas at Arlington, USA), Tejo V. Bheemasetti (The University of Texas at Arlington, USA), Haifeng Zou (Southeast University, China), Xinbao Yu (The University of Texas at Arlington, USA), Aravind Pedarla (The University of Texas at Arlington, USA), and Guojun Cai (Southeast University, China)
Copyright: © 2016
|Pages: 32
DOI: 10.4018/978-1-4666-9479-8.ch008
Abstract
Spatial variability in soil properties is still in the exploratory stage and, despite of an increase in probabilistic and statistical analysis, many challenges remain in using spatial variability of soil properties in practical designs. This chapter addresses the problem of how to incorporate spatial variability of soil properties by using Geostatistics. Existing researches in variability analysis tend to focus on the distribution of the soil properties, reliability based design and simulation of random fields. However, there is limited evidence that researchers have approached the issue of spatial variability in soil properties. Consequently, the aim of this chapter is to develop a framework for incorporating spatial variability in soil properties in prediction analysis and how it could be applied to infrastructure design. The developed framework is validated by performing spatial variability analysis of soil strength parameters evaluated from the piezocone penetration test data.