Sequential Gaussian simulation for geosystems modeling:A machine learning approach

作     者：Tao Bai Pejman Tahmasebi Tao Bai;Pejman Tahmasebi

作者机构：College of Engineering and Applied ScienceUniversity of WyomingLaramieWY 82071USA 

出 版 物：《Geoscience Frontiers》 (地学前缘（英文版）)

年 卷 期：2022年第13卷第1期

页      面：1-14页

核心收录：

学科分类：12[管理学] 081801[工学-矿产普查与勘探] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0818[工学-地质资源与地质工程] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：financial support from the University of Wyoming the School of Energy Resources for this research is greatly acknowledged 

主　　题：Artificial intelligence Uncertainty Geosystems Statistical modeling 

摘      要：Sequential Gaussian Simulation(SGSIM)as a stochastic method has been developed to avoid the smoothing effect produced in deterministic methods by generating various stochastic *** of the main issues of this technique is,however,an intensive computation related to the inverse operation in solving the Kriging system,which significantly limits its application when several realizations need to be produced for uncertainty *** this paper,a physics-informed machine learning(PIML)model is proposed to improve the computational efficiency of the *** this end,only a small amount of data produced by SGSIM are used as the training dataset based on which the model can discover the spatial correlations between available data and unsampled *** achieve this,the governing equations of the SGSIM algorithm are incorporated into our proposed *** quality of realizations produced by the PIML model is compared for both 2D and 3D cases,visually and ***,computational performance is evaluated on different grid *** results demonstrate that the proposed PIML model can reduce the computational time of SGSIM by several orders of magnitude while similar results can be produced in a matter of seconds.