Geostatistical development

David, M. "Developments in Geomathematics 2, Geostatistical Ore Reserve Estimation" Elsevier Scientific Publishing Company New York, 1977. 

Erickson, T. A. Development and Application of Geostatistical Methods to Modeling Spatial Variation in Snowpack Properties, Front Range, Colorado. Ph.D. Dissertation, University of Colorado, Boulder, CO, 2004. 

Siegel M. D., Eeckie J. O., Phillips S. L., and Kelly W. R. (1989) Development of a methodology of geochemical sensitivity analysis for performance assessment. In Proceedings of the Conference on Geostatistical, Sensitivity, and Uncertainty Methods for Ground-Water Flow and Radionuclide Transport Modeling. San Francisco, CA, Battdle, pp. 189-211. 

Hydraulic parameters like kf values and the colmatation layer thickness were derived from measurements, a digitized map of the alluvial soil thickness and geostatistic calculations. The classified parameters were assigned to each grid point of the geometric ditch model. From this information, a specific data model was developed and connected with an imcomplex self constructed model based on the Darcy equation to calculate the exfiltration rates (Fig. 8.9). 

Currently, there is a need to develop alternative techniques for the analysis of spatial data on forest health. The existing techniques do not take into account the complex nature of any possible relationships between air pollution and forest health and new methods are urgently required. The use of geostatistics has been explored by Innes and Boswell (1989) and offers considerable potential other techniques undoubtedly exist but have yet to be tested. 

The Bayesian approach is beginning to come into favor since the importance of site characterization and sampling strategies has increased. This approach is similar to the geostatistical approach (kriging) in its use to develop statistically realistic descriptions of sites to aid in initial data collection and site exploration when there is a scarcity of hard data (6, 72). The Bayesian approach may provide the general framework to update parameter uncertainty when additional data are available, and to evaluate its effect on estimation or decision (13). 

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