Debiasing For Improved Inference Of The One Point Statistic

Strategic project decisions are based on the distributions global variables, for example, total mineable resource, or recoverable oil volume. These global variables distributions are very sensitive to the input one point statistic, that is, histogram and rock type proportions. Representivity in the one point statistic retains significance in all spatial models. Spatial sampling bias and nonrepresentative sampling complicate this process of building representative one point statistics. This work outlines the cause of bias, the inability of standard declustering to correct for this bias and two methods for correcting bias in the one point statistics: "trend modeling for debiasing" and "debiasing by qualitative data". An example is presented of each technique based on a poly metallic data set.