气候变化领域集成服务门户(CCPortal): Locality-based 3-D multiple-point statistics reconstruction using 2-D geological cross sections

CCPortal

DOI	10.5194/hess-22-6547-2018
	Locality-based 3-D multiple-point statistics reconstruction using 2-D geological cross sections
	Chen Q.; Mariethoz G.; Liu G.; Comunian A.; Ma X.
发表日期	2018
ISSN	1027-5606
起始页码	6547
结束页码	6566
卷号	22 期号:12
英文摘要	Multiple-point statistics (MPS) has shown promise in representing complicated subsurface structures. For a practical three-dimensional (3-D) application, however, one of the critical issues is the difficulty in obtaining a credible 3-D training image. However, bidimensional (2-D) training images are often available because established workflows exist to derive 2-D sections from scattered boreholes and/or other samples. In this work, we propose a locality-based MPS approach to reconstruct 3-D geological models on the basis of such 2-D cross sections (3DRCS), making 3-D training images unnecessary. Only several local training subsections closer to the central uninformed node are used in the MPS simulation. The main advantages of this partitioned search strategy are the high computational efficiency and a relaxation of the stationarity assumption. We embed this strategy into a standard MPS framework. Two probability aggregation formulas and their combinations are used to assemble the probability density functions (PDFs) from different subsections. Moreover, a novel strategy is adopted to capture more stable PDFs, where the distances between patterns and flexible neighborhoods are integrated on multiple grids. A series of sensitivity analyses demonstrate the stability of the proposed approach. Several hydrogeological 3-D application examples illustrate the applicability of the 3DRCS approach in reproducing complex geological features. The results, in comparison with previous MPS methods, show better performance in portraying anisotropy characteristics and in CPU cost. © Author(s) 2018.
语种	英语
scopus关键词	Computational efficiency; Geology; Probability density function; Sensitivity analysis; Three dimensional computer graphics; 3-d geological models; Application examples; Geological features; Multiple-point statistics; Probability density functions (PDFs); Search strategies; Subsurface structures; Threedimensional (3-d); Image reconstruction; anisotropy; density; geology; image; model; sensitivity analysis; simulation; statistical analysis
来源期刊	Hydrology and Earth System Sciences
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/159819
作者单位	Chen, Q., School of Computer Science, China University of Geosciences, Wuhan, 430074, China, Institute of Earth Surface Dynamics, University of Lausanne, Lausanne, 1015, Switzerland, Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan, 430074, China; Mariethoz, G., Institute of Earth Surface Dynamics, University of Lausanne, Lausanne, 1015, Switzerland; Liu, G., School of Computer Science, China University of Geosciences, Wuhan, 430074, China, Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan, 430074, China; Comunian, A., Dipartimento di Scienze della Terra 'A. Desio', Università degli Studi di Milano, Milan, Italy; Ma, X., Department of Computer Science, University of Idaho, 875 Perimeter Drive MS 1010, Moscow, ID 83844-1010, United States
推荐引用方式 GB/T 7714	Chen Q.,Mariethoz G.,Liu G.,et al. Locality-based 3-D multiple-point statistics reconstruction using 2-D geological cross sections[J],2018,22(12).
APA	Chen Q.,Mariethoz G.,Liu G.,Comunian A.,&Ma X..(2018).Locality-based 3-D multiple-point statistics reconstruction using 2-D geological cross sections.Hydrology and Earth System Sciences,22(12).
MLA	Chen Q.,et al."Locality-based 3-D multiple-point statistics reconstruction using 2-D geological cross sections".Hydrology and Earth System Sciences 22.12(2018).