Groundwater Level Mapping Using Multiple-Point Geostatistics

Abstract

Abstract:Methods based on two-point geostatistics have been routinely used to interpolate random variables such as groundwater level and concentration and to estimate their values at un-sampled locations. These methods use the observed data to analyze spatial two-point correlations and ignore the higher order moments that may play a key role in the characterization of complex patterns. In this work, a multiple-point geostatistics method is applied to interpolate groundwater level data. To do this, the ensemble simulated groundwater level maps after modeling full physics are used as training images to derive the multiple correlations. 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 groundwater level data in a complex geological formation.

Spatiotemporal Geostatistical Analysis of Groundwater Level in Aquifer Systems of Complex Hydrogeology

“…Among the different existing interpolation methods, geostatistical analysis has been used as a management and decision tool to reveal the spatial and temporal structure of groundwater level and determine the values for the points where measurements are not made or are not feasible to measure due to significant restrictions (Ahmadi & Sedghamiz, 2008; Kitanidis, 1997; Li & Huang, 2016; Rivest et al., 2008; Ruybal et al., 2019; Sun et al., 2009; Varouchakis, 2019). A Bayesian approach is preferred over a classical one since it lets to deal with the parameters and the uncertainty in the model (Guardiola‐Albert & Pardo‐Igúzquiza, 2011; Varouchakis et al., 2019b).…”

<strong>The Concept of Training Image-Based Geostatistics for Spatial Distribution of Hydraulic Conductivity</strong>

“…Kriging is the optimal technique for groundwater level interpolation in a real case study [5]. In all literature studies, two-point interpolation methods are applied to quantify the spatial correlations only, but for complex formations composed of geologic heterogeneity, these methods interpolation may be inappropriate [6]. These methods cannot reproduce the interconnected, curvilinear geometries characteristic of many heterogeneous complex patterns.…”