Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/8638
Title: Efficient simulation of (log)normal random fields for hydrogeological applications
Authors: Kyriakidis, Phaedon 
Gaganis, Petros 
Major Field of Science: Engineering and Technology
Field Category: Environmental Engineering
Keywords: Geostatistics;Monte Carlo simulation;Latin hypercube;Mahalanobis distance;Modflow;MT3D
Issue Date: 18-Jun-2013
Source: Mathematical Geosciences, 2013, vol. 45, no. 5, pp. 531–556
Volume: 45
Issue: 5
Start page: 531
End page: 556
Journal: Mathematical Geosciences 
Abstract: Two methods for generating representative realizations from Gaussian and lognormal random field models are studied in this paper, with term representative implying realizations efficiently spanning the range of possible attribute values corresponding to the multivariate (log)normal probability distribution. The first method, already established in the geostatistical literature, is multivariate Latin hypercube sampling, a form of stratified random sampling aiming at marginal stratification of simulated values for each variable involved under the constraint of reproducing a known covariance matrix. The second method, scarcely known in the geostatistical literature, is stratified likelihood sampling, in which representative realizations are generated by exploring in a systematic way the structure of the multivariate distribution function itself. The two sampling methods are employed for generating unconditional realizations of saturated hydraulic conductivity in a hydrogeological context via a synthetic case study involving physically-based simulation of flow and transport in a heterogeneous porous medium; their performance is evaluated for different sample sizes (number of realizations) in terms of the reproduction of ensemble statistics of hydraulic conductivity and solute concentration computed from a very large ensemble set generated via simple random sampling. The results show that both Latin hypercube and stratified likelihood sampling are more efficient than simple random sampling, in that overall they can reproduce to a similar extent statistics of the conductivity and concentration fields, yet with smaller sampling variability than the simple random sampling.
URI: https://hdl.handle.net/20.500.14279/8638
ISSN: 18748961
18748953
DOI: 10.1007/s11004-013-9470-5
Rights: © Springer
Type: Article
Affiliation : University of Aegean 
Publication Type: Peer Reviewed
Appears in Collections:Άρθρα/Articles

CORE Recommender
Show full item record

SCOPUSTM   
Citations

7
checked on Nov 9, 2023

WEB OF SCIENCETM
Citations 50

5
Last Week
0
Last month
0
checked on Oct 29, 2023

Page view(s)

326
Last Week
1
Last month
2
checked on Oct 31, 2024

Google ScholarTM

Check

Altmetric


Items in KTISIS are protected by copyright, with all rights reserved, unless otherwise indicated.