Incorporating hierarchy as an explicit and testable component of the modeling process contributes to more transparent, robust, and decision-aware geostatistical practices. ACKNOWLEDGEMENTS The authors gratefully acknowledge the financial support provided by the Coordination for the Improvement of Higher Education Personnel (CAPES) and the National Council for Scientific and Technological Development (CNPq), as well as the institutional support of the Graduate Program in Mining, Metallurgical and Materials Engineering (PPGE3M) and the Laboratory of Mineral Research and Mine Planning (LPM) at the Federal University of Rio Grande do Sul (UFRGS). REFERENCES Caers, J. (2011). Modeling Uncertainty in the Earth Sciences. Wiley-Blackwell. Deutsch, C. V., & Journel, A. G. (1998). GSLIB: Geostatistical Software Library and User’s Guide (2nd ed.). New York: Oxford University Press. Goovaerts, P. (1997). Geostatistics for Natural Resources Evaluation. New York: Oxford University Press. Journel, A. G., & Huijbregts, C. J. (1978). Mining Geostatistics. London: Academic Press. Journel, A. G. (1983). Nonparametric estimation of spatial distributions. Mathematical Geology, 15(3), 445 - 468. Journel, A. G., & Alabert, F. (1989). Non-Gaussian data expansion in the Earth sciences. Terra Nova, 1(2), 123–134. Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246 - 1266. Matheron, G. (1971). The Theory of Regionalized Variables and Its Applications. Fontainebleau: École Nationale Supérieure des Mines de Paris. Shannon, C. E. (1948). A Mathematical Theory of Communication. Bell System Technical Journal, 27(3), 379–423.
RkJQdWJsaXNoZXIy MTM0Mzk2