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首页> 外文期刊>Journal Of The South African Institute Of Mining & Metallurgy >Capping and kriging grades with long-tailed distributions
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Capping and kriging grades with long-tailed distributions

机译：具有长尾分布的封顶和克里金牌号

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摘要

Variogram analysis and kriging lack robustness in the presence or outliers and data with long-tailed distributions, which often arises when estimating grades in precious metal deposits. The capping technique, consisting of truncating the data to some top-cut grade, is widely used in order to mitigate (he influence of the values in the upper tail of the distribution. However, this procedure omits part of the grade variability and is likely to provoke a bias in the estimates. To avoid these issues, a recently proposed approach is to decompose the grade of interest into three components (the truncated grade, a weighted indicator above the top cut grade, and a zero mean residual and jointly estimate the truncated grade and the indicator by cokriging. This approach is attractive as it provides unbiased grade estimates, allows choosing the 'optimal' top-cut value, and essentially works with truncated and indicator data, thus avoiding the use of outlying values for calculating sample variograms and performing spatial interpolation.This work presents an application of this approach to a disseminated gold deposit that has been identified through exploration drilling. The effect of using an indicator covariate is assessed through leave-one-oul cross-validation, by comparing the grade estimates with the true grades and with the grade estimates obtained with the conventional capping approach, which considers only the truncated grade as the variable of interest. As a result, cokriging the truncated grade and the indicator above top-cut grade outperforms the conventional capping approach, yielding significantly more accurate estimates. A few complementary guidelines are provided for validating the model hypotheses and for the implementation of cokriging.
机译：变量图分析和克里金法在存在或存在异常值以及长尾分布的数据时缺乏鲁棒性，这在估算贵金属矿床的品位时通常会出现。覆盖技术（包括将数据截断到一些最上层的等级）被广泛使用，以减轻（受分布上尾值的影响。但是，此过程忽略了部分等级变异性，并且可能为避免这些问题，最近提出的一种方法是将感兴趣的等级分解为三个部分（截断的等级，高于最高等级的加权指标以及零均值残差并共同估算）截断的等级和指标通过共克里金法，这种方法很有吸引力，因为它提供了不偏不倚的等级估计值，允许选择“最佳”顶切值，并且本质上适用于截断和指标数据，从而避免了使用偏远的值来计算样本变异函数这项工作介绍了这种方法在已通过勘探钻探确定的分散金矿床中的应用。使用指标协变量的t是通过留一生交叉验证来评估的，方法是将等级估计值与真实等级以及通过常规封顶方法获得的等级估计值进行比较，传统封顶方法仅将截断的等级视为关注变量。结果，对截断的坡度和顶部指标上方的指标进行共克里格胜过传统的封顶方法，从而得出了更为准确的估计值。提供了一些补充准则，以验证模型假设和实施协同克里格。

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