The variogram model is one of the most relevant parameters in geostatistical estimation and simulation methods. The sample variogram is inferred from available data, which may be subject to spatial bias and proportional effect. Over this sample variogram, a licit variogram model is fit and is carried through the process of estimation and/or simulation of the random function usually without rega...

Scientists need appropriate spatial-statistical models to account for the unique features of stream network data. Recent advances provide a growing methodological toolbox for modelling these data, but general-purpose statistical software has only recently emerged, with little information about when to use different approaches. We implemented a simulation study to evaluate and validate geostatis...

introduction the knowledge about spatial variability of precipitation is a key issue for regionalization in hydro-climatic studies. measurements of meteorological parameters by the traditional methods require a dense rain gauge network. but, due to the topography and cost problems, it is not possible to create such a network in practice. in these cases the spatial distribution pattern of precip...

This paper presents an overview of geostatistical simulation with particular focus on aspects of importance to its application for quantification of risk in the mining industry. Geostatistical simulation is a spatial extension of the concept of Monte Carlo simulation. In addition to reproducing the data histogram, geostatistical simulations also honour the spatial variability of data, usually c...

[1] We investigate, through numerical experiments, the viability of three-dimensional transient hydraulic tomography (3DTHT) for identifying the spatial distribution of groundwater flow parameters (primarily, hydraulic conductivity K) in permeable, unconfined aquifers. To invert the large amount of transient data collected from 3DTHT surveys, we utilize an iterative geostatistical inversion str...

This article gives a brief overview of classical spatial statistics. Three main types of spatial data are considered: geostatistical data, lattice data, and spatial point patterns. Modeling themes common to all three data types are emphasized. In addition, some basic structural identification procedures for each data type are described. Finally, maximum likelihood estimation and spatial predict...

This study presents a stochastic, threedimensional characterization of a heterogeneous hydraulic conductivity field within the Hanford 300 Area, Washington, USA, by assimilating large-scale, constant-rate injection test data with small-scale, three-dimensional electromagnetic borehole flowmeter (EBF) measurement data. We first inverted the injection test data to estimate the transmissivity fiel...

Present address: Lafarge Canada, Corporate Technical Services, 6150 ave. Royalmount, Montréal, Québec, Canada H4P 2R3 Abstract This paper describes a geostatistical technique based on conditional simulations to assess confidence intervals of local estimates of lake pH values on the Canadian Shield. This geostatistical approach has been developed to deal with the estimation of phenomena with a s...

Due to the existence of a constant sum of constraints, the geochemical data is presented as the compositional data that has a closed number system. A closed number system is a dataset that includes several variables. The summation value of variables is constant, being equal to one. By calculating the correlation coefficient of a closed number system and comparing it with an open number system, ...

An accurate estimation of soil organic matter (SOM) content for spatial non-point prediction is an important driving force for the agricultural carbon cycle and sustainable productivity. This study proposed a hybrid geostatistical method of extreme learning machine-ordinary kriging (ELMOK), to predict the spatial variability of the SOM content. To assess the feasibility of ELMOK, a case study w...