基于软硬数据的多点地质统计法在图像统计信息重构中的应用研究

Research on Statistical Information Reconstruction of Images Based on Multiple-Point Geostatistics Integrating Soft Data with Hard Data

仅使用硬数据或无条件数据时,图像统计信息的重构会比较困难而且精度不高.如果在重构过程中加入软数据,则可以提高图像重构的准确性.结合使用软数据和硬数据,提出了一种利用多点地质统计法重构图像统计信息的方法.该方法在再现训练图像特征模式的过程中,将软数据和硬数据同时作为条件数据,因此可以提高重构图像的精度.实验表明,与仅使用硬数据和无条件数据的情况相比,该方法重构的图像具有与真实体数据更为相似的结构特征.

In many fields,there are two types of data： hard data and soft data. Soft data typically provide an extensive coverage of the field under study although with low resolution. It is necessary to condition the reconstructed models to all these different types of data so as to improve the accuracy. The statistical information reconstruction of images will be difficult and inaccurate when only hard data are available or there are no conditional data. Accuracy of reconstructed images can be improved through the use of soft data during the process of reconstruction. Integrating soft data with hard data,a method based on MPS （multiple-point geostatistics） is proposed to reconstruct statistical information of images. By reproducing high-order statistics,MPS allows capturing structures from a training image,and then anchoring them to the specific model data. A training image is a numerical prior model which contains the structures and relationship existing in realistic models. During the process of regenerating characteristic patterns in a training image,the accuracy of reconstructed images is improved,using both soft data and hard data as conditional data. The experimental results show that,compared with the unconditional reconstruction images and the reconstructed images using only hard data,the structure characteristics in reconstructed images using this method are similar to those obtained from real volume data.