A Multi-Point Geostatistical Modeling Method Based on 2D Training Image Partition Simulation

Zhao, Yifei, Chen, Jianhong, Yang, Shan, He, Kang, 島田, 英樹, 笹岡, 孝司

doi:10.3390/math11244900

In this paper, a multi-point geostatistical (MPS) method based on variational function partition simulation is proposed to solve the key problem of MPS 3D modeling using 2D training images. The new method uses the FILTERSIM algorithm framework, and the variational function is used to construct simulation partitions and training image sequences, and only a small number of training images close to the unknown nodes are used in the partition simulation to participate in the MPS simulation. To enhance the reliability, a new covariance filter is also designed to capture the diverse features of the training patterns and allow the filter to downsize the training patterns from any direction; in addition, an information entropy method is used to reconstruct the whole 3D space by selecting the global optimal solution from several locally similar training patterns. The stability and applicability of the new method in complex geological modeling are demonstrated by analyzing the parameter sensitivity and algorithm performance. A geological model of a uranium deposit is simulated to test the pumping of five reserved drill holes, and the results show that the accuracy of the simulation results of the new method is improved by 11.36% compared with the traditional MPS method.

A Multi-Point Geostatistical Modeling Method Based on 2D Training Image Partition Simulation

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A Multi-Point Geostatistical Modeling Method Based on 2D Training Image Partition Simulation

説明

収録刊行物

キーワード

詳細情報 詳細情報について

書き出し

問題の指摘

詳細情報詳細情報について