资源环境科技发展态势分析平台

Dealing with complex and geologically realistic modeling of subsurface systems requires detailed spatial data sets. Such a big data can be usually provided through an image. Despite various developments, the training image-based techniques are still not well-designed for modeling multiscale and complex structures. Pixel-based methods honor the conditioning point data with poor reproduction of large-scale features, while some other techniques, termed pattern-based, represent a superior reproduction of long-range and complex structures and being biased around the conditioning data. In this paper, a new look at the geostatistical modeling using a hybrid pattern-pixel-based simulation (HYPPS) is proposed, wherein the pixel and pattern-based techniques are used simultaneously. A perfect reproduction of the conditioning point data is achieved using the proposed HYPPS method. The algorithm is developed for single and multivariate simulations. This method is applied on different 2-D/3-D categorical data and the results show significant improvement over the previous techniques.