BAYESIAN MAXIMUM-ENTROPY ANALYSIS AND MAPPING - A FAREWELL TO KRIGINGESTIMATORS

The Bayesian Maximum Entropy (BME) method of spatial analysis and mapp ing provides definite rules for incorporating prior information, hard and soft data into the mapping process. It has certain unique features that make it a loyal guardian of plausible reasoning under conditions of uncertainty. BME is a general approach that does not make any assu mptions regarding the linearity of the estimator, the normality of the underlying probability laws, or the homogeneity of the spatial distri bution. By capitalizing on various sources of information and data, BM E introduces an epistemological framework that produces predictive map s that are more accurate and in many cases computationally more effici ent than those derived by traditional techniques. In fact, kriging tec hniques can be derived as special cases of the BME approach, under res trictive assumptions regarding the prior information and the data avai lable. BME is a more rigorous approach than indicator kriging for inco rporating soft data. The BME formulation, in fact, applies in a spatia l or a spatiotemporal domain and its extension to the case of block an d vector random fields is straightforward. New theoretical results are presented and numerical examples are discussed, which use the BME app roach to account for important sources of knowledge in a systematic ma nner. BME can be useful in practical situations in which prior informa tion can be used to compensate for the limited amount of measurements available (e.g., preliminary of feasibility study levels) or soft data are available that can be combined with hard data to improve mapping significantly. BME may be then viewed as an effort towards the develop ment of a more general framework of spatial/temporal analysis and mapp ing, which includes traditional geostatistics as its limiting case, an d it also provides the means to derive novel results that could nor be obtained by traditional geostatistics.