Filter-based classification of training image patterns for spatial simulation

被引：330

作者：

Zhang, Tuanfeng ^{[1
]}

Switzer, Paul ^{[1
]}

Journel, Andre ^{[1
]}

机构：

[1] Stanford Univ, Dept Geol & Environm Sci, Stanford, CA 94305 USA

来源：

MATHEMATICAL GEOLOGY | 2006年 / 38卷 / 01期

关键词：

multiple-point simulation; geostatistics; data conditioning; multiple grids;

D O I：

10.1007/s11004-005-9004-x

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

Multiple-point simulation, as opposed to simulation one point at a time, operates at the pattern level using a priori structural information. To reduce the dimensionality of the space of patterns we propose a multi-point filtersim algorithm that classifies structural patterns using selected filter statistics. The pattern filter statistics are specific linear combinations of pattern pixel values that represent directional mean, gradient, and curvature properties. Simulation proceeds by sampling from pattern classes selected by conditioning data.

引用

页码：63 / 80

页数：18