Preprint Technical Note Version 1 Preserved in Portico This version is not peer-reviewed

The Concept of Training Image-Based Geostatistics for Spatial Distribution of Hydraulic Conductivity

Version 1 : Received: 24 February 2021 / Approved: 26 February 2021 / Online: 26 February 2021 (12:47:53 CET)

How to cite: Hussain, F.; Wu, R. The Concept of Training Image-Based Geostatistics for Spatial Distribution of Hydraulic Conductivity. Preprints 2021, 2021020618 (doi: 10.20944/preprints202102.0618.v1). Hussain, F.; Wu, R. The Concept of Training Image-Based Geostatistics for Spatial Distribution of Hydraulic Conductivity. Preprints 2021, 2021020618 (doi: 10.20944/preprints202102.0618.v1).

Abstract

Hydraulic conductivity is the key and one of the most uncertain parameters in groundwater modeling. The grid based numerical simulation require spatial distribution of sampled hydraulic conductivity at un-sampled locations in the study area. This spatial interpolation has been routinely performed using variogram based models (two-point geostatistics methods). These traditional techniques fail to capture the complex geological structures, provides smoothing effects and ignore the higher order moments of subsurface heterogeneities. In this work, a multiple-point geostatistics (MPS) method is applied to interpolate hydraulic conductivity data which will be further used in WASH123D numerical groundwater simulation model for regional smart groundwater management. To do this, MPS need ‘training images (TIs) as a key input. TI is a conceptual model of subsurface geological heterogeneity which was developed by using concept of ages, topographic slope as an index criteria and knowledge of geologist. After considerations of full physics of study area, an example shows the advantages of using multiple-point geostatistics compared with the traditional two-point geostatistics methods (such as Kriging) for the interpolation of hydraulic conductivity data in a complex geological formation.

Subject Areas

Interpolation; Hydraulic Conductivity; Multi-Point Geostatistics; Training Image

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