Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/137914
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Type: | Journal article |
Title: | Geostatistics in the Presence of Multivariate Complexities: Comparison of Multi-Gaussian Transforms |
Author: | Abulkhair, S. Dowd, P.A. Xu, C. |
Citation: | Mathematical Geosciences, 2023; 55(6):713-734 |
Publisher: | Springer |
Issue Date: | 2023 |
ISSN: | 1874-8961 1874-8953 |
Statement of Responsibility: | Sultan Abulkhair, Peter A. Dowd, Chaoshui Xu |
Abstract: | One of the most challenging aspects of multivariate geostatistics is dealing with complex relationships between variables. Geostatistical co-simulation and spatial decorrelation methods, commonly used for modelling multiple variables, are ineffective in the presence of multivariate complexities. On the other hand, multi-Gaussian transforms are designed to deal with complex multivariate relationships, such as non-linearity, heteroscedasticity and geological constraints. These methods transform the variables into independent multi-Gaussian factors that can be individually simulated. This study compares the performance of the following multi-Gaussian transforms: rotation based iterative Gaussianisation, projection pursuit multivariate transform and flow transformation. Case studies with bivariate complexities are used to evaluate and compare the realisations of the transformed values. For this purpose, commonly used geostatistical validation metrics are applied, including multivariate normality tests, reproduction of bivariate relationships, and histogram and variogram validation. Based on most of the metrics, all three methods produced results of similar quality. The most obvious difference is the execution speed for forward and back transformation, for which flow transformation is much slower. |
Keywords: | Rotation based iterative Gaussianisation Projection pursuit multivariate transform Flow transformation Minimum/maximum autocorrelation factors Complex bivariate relationships |
Description: | Published 05 April 2023. |
Rights: | © The Author(s) 2023. Open Access. This article is licensed under a Creative Common sAttribution 4.0 International License,which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
DOI: | 10.1007/s11004-023-10056-y |
Grant ID: | http://purl.org/au-research/grants/arc/IC190100017 |
Published version: | http://dx.doi.org/10.1007/s11004-023-10056-y |
Appears in Collections: | Civil and Environmental Engineering publications |
Files in This Item:
File | Description | Size | Format | |
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hdl_137914.pdf | Published version | 3.02 MB | Adobe PDF | View/Open |
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