A Comparison of the Sequential Gaussian and Markov-Bayes Simulation Methods for Small Samples

We compared the performance of sequential Gaussian simulation (sGs) and Markov-Bayes simulation (MBs) using relatively small samples taken from synthetic datasets. A moderate correlation (approximately r = 0.70) existed between a continuous primary variable and a continuous secondary variable. Given the small sample sizes, our objective was to determine whether MBs, with its ability to incorporate the secondary information, would prove superior to SgS. A split-split-plot computer experiment was conducted to compare the two simulation methods over a variety of primary and secondary sample sizes as well as spatial correlations. Using average mean square prediction error as a measure of local performance, sGs and MBs were roughly equivalent for random fields with short ranges (2 m). As range increased (15 m) the average mean square prediction error for sGs was less than or equal to that for MBs, even when number of noncollocated secondary observations was twice the number of collocated observations. Median variance within nonoverlapping subregions was used as a measure of the local heterogeneity or surface texture of the image. In most situations sGs images more faithfully reflected the true local heterogeneity, while MBs was more erratic, sometimes oversmoothing and sometimes undersmoothing.     

不同地质统计方法在确定渗透系数场中的对比研究

应用地质统计方法研究渗透系数场的空间变异性。利用MMR含水层场地实测数据,通过去类分析、特异值处理、正态变换,逐步逼近研究区渗透系数的稳健变差函数,得到三维渗透系数场的几何各向异性套合模型。在此基础上,采用普通克里格法和指示克里格法、高斯序列模拟法和指示序列模拟法分别对数据进行插值和条件模拟。最后结合具体的地质条件,对四种方法在渗透系数场生成中的应用进行对比分析和评价。