Bayesian compressive sensing or sampling (BCS) can be used for handling sparse measurements in geo-sciences and engineering. Site characterization is indispensable to good geotechnical or rock engineering practice as every site is unique, but technical, budget, time, or access constraints, typically result in only a tiny fraction of the underground soil and rock in a site being visually inspected, sampled or tested. This book introduces BCS as a highly efficient spatial data analytic and simulation method for the efficient modelling of spatial geo-data from sparse measurements. It shows how to quantify reliability and uncertainty, and how to optimize site characterization. It sets out the necessary theory and computational tools for setting up and solving a sparse spatial data modelling problem using BCS.

This book suits graduate students, academics, researchers, and engineers interested in site characterization from sparse measurements in geotechnical and rock engineering, and also those modelling other spatially varying phenomena such as air quality data, soil or water pollution data, and meteorological data. This is supplemented with Analytics of Sparse Spatial Data using Bayesian Compressive Sampling/Sensing software, with the examples, and enables hands-on experience of spatial data analytics and simulation using sparse measurements.