Seismic site effects are influenced mainly by geospatial uncertainties corresponding to geological or geotechnical spatial variance. Therefore, the development of a geospatial database is essential to characterize site-specific geotechnical information in multiscale areas and to optimize geospatial zonation methods with potentially high degrees of spatial variability based on trial-and-error ge...

Reliable prediction of subsurface flow and contaminant transport depends on the accuracy with which the values and spatial distribution of process-relevant model parameters can be identified. Successful characterization methods for complex soil systems are based on (1) an adequate parameterization of the subsurface, capable of capturing both random and structured aspects of the heterogeneous sy...

The Douglas-Þr beetle, Dendroctonus pseudotsugae Hopkins, causes considerable mortality in Douglas-Þr, Pseudotsuga menziesii (Mirb.) Franco, forests. Within-stand distribution of mortality was examined in affected stands using geostatistical techniques. A 10 3 10 m grid was established in two 4-ha study sites. Live and beetle-killed host basal area was measured at each node. In a 16-ha stand, a...

The coupled ̄ow-mass transport inverse problem is formulated using the maximum likelihood estimation concept. An evolutionary computational algorithm, the genetic algorithm, is applied to search for a global or near-global solution. The resulting inverse model allows for ̄ow and transport parameter estimation, based on inversion of spatial and temporal distributions of head and concentration meas...

[1] Estimation under model uncertainty remains a practical concern in many scientific and engineering fields. A commonly encountered example in groundwater remediation is the contaminant source identification problem. Like many other inverse problems, contaminant source identification is inherently ill posed and is sensitive to both data and model uncertainties. Model uncertainties, which may b...

Modelling spatial covariance is an essential part of all geostatistical methods. Traditionally, parametric semivariogram models are fit from available data. More recently, it has been suggested to use nonparametric correlograms obtained from spatially complete data fields. Here, both estimation techniques are compared. Nonparametric correlograms are shown to have a substantial negative bias. No...

This paper presents a method for establishing an optimal network design for the estimation of areal averages of rainfall events. The problem consists of minimising an objective function which includes both the accuracy of the areal mean estimation (as expressed by the kriging variance of estimation) and the economic cost of the data collection. The well known geostatistical variance-reduction m...

The purpose of this work is to compare the linear and non-linear kriging methods in the mineral resource estimation of the Qolqoleh gold deposit in Saqqez, NW Iran. Considering the fact that the gold distribution is positively skewed and has a significant difference with a normal curve, a geostatistical estimation is complicated in these cases. Linear kriging, as a resource estimation method, c...

In the domain of oil exploration, geostatistical methods aim at simulating petrophysical properties in a 3D grid model of reservoir. Generally, only a small amount of cells are populated with properties. Roughly speaking, the question is: which properties to give to cell c, knowing the properties of n cells at a given distance from c? Obviously, the population of the whole reservoir must be com...