论文标题
在多点统计的背景下，地理模型分辨率增强
Geostatistical Model Resolution Enhancement in the Context of Multiple-Point Statistics
论文作者
论文摘要
当前基于多点的模拟实现在训练图像的规模上生成地统计模型。假设这些类别在较小的尺度上是独有的。本文的目的是以比可用培训图像更高的分辨率生成具有多点统计（MP）的模型。本文通过研究基于MPS的模型中空间结构的规模依赖性来解决模型分辨率的增强 - 将较小规模的MPS从较大规模的MPS推断出来，以及（2）将训练图像直接重新确定为较小的规模。第一种方法研究了MPS概率。记录了使用高阶统计数据来表征较小规模可变性的许多挑战。本文通过主张直接重新缩放训练图像，以更高的分辨率生成模型。
Current multiple-point based simulations implementations generate geostatistical models at the scale of the training image; there is an assumption that the categories are exclusive at smaller scales. The goal of this paper is to generate models with multiple-point statistics (MPS) at a higher resolution than that of the available training image. This paper addresses model resolution enhancement by studying the scale-dependence of spatial structure in MPS based models -- extrapolating the smaller scale MPS from the larger scale MPS, and (2) rescaling the training image directly to the smaller scale. The first approach investigates the MPS probabilities. A number of challenges in characterizing smaller scale variability using high-order statistics are documented. The paper concludes by advocating the direct rescaling of the training image to generate models at higher resolution.
