Spatial Modeling Principles in Earth Sciences

The second edition of this work expands earlier coverage with fresh chapters on modeling temporal and point uncertainty, sampling, and deterministic approaches. It surveys spatial modeling methods used across the earth sciences and includes techniques the author developed himself. Collecting reliable geological, hydrogeological, meteorological and hydrological data is costly and labor-intensive, and every measurement carries risk that must be assessed. The book argues that spatial simulation is needed to capture how features such as fracture density, rock quality and grain size vary within rock and porous media. Its main focus is Kriging, presented in a detailed but approachable way, together with the physical reasoning, field examples and sample interpretations that accompany it. Since Kriging emerged in the 1960s, it has branched into related methods, including cumulative SV, point CSV and the spatial dependence function. The text explains each of these and shows how they apply to geology, earthquakes, meteorology and hydrology. Several alternatives to Kriging are also covered, with the steps needed to use them. Simpler spatial prediction techniques are reviewed against current literature, and their connections to the more advanced methods are made clear. It is aimed at students, researchers and practitioners in geology, geography, hydrology, meteorology, environmental work, image processing and spatial modeling.