This paper discusses the use of robust geostatistical methods on a data set of rainfall measurements for Switzerland. The variables are detrended via nonparametric estimation penalized with a smoothing parameter. The optimal trend is computed with a smoothing parameter based on cross-validation. The variogram is then estimated by a highly robust estimator of scale. The parametric variogram mode...

Geostatistical analyses were first developed in the 1950's as a result of interest in areal or block averages for ore reserves in the mining industry. Today, variogram estimation and spatial prediction (kriging) span all sciences where data exhibit spatial correlation. Theoretical properties of the spatial process are addressed under the distribution-free and likelihood-based perspectives. Stre...

This chapter proposes a clear methodology on how to use machine learning algorithms for spatial data analysis in order to avoid any bias and eventually obtain fair estimation of their performance on new data. Four different machine learning algorithms are presented, namely multilayer perceptrons (MLP), mixture of experts (ME), support vector regression (SVR) and a local version of the latter (l...

In mining projects, all uncertainties associated with a project must be considered to determine the feasibility study. Grade uncertainty is one of the major components of technical uncertainty that affects the variability of the project. Geostatistical simulation, as a reliable approach, is the most widely used method to quantify risk analysis to overcome the drawbacks of the estimation methods...

soil temperature is one of the most effective parameters for plant growth. although the spatiotemporal monitoring of soil temperature is important for providing an optimum condition to maximize the yield efficiency, indirect measurements for zoning  of soil temperature are essential due to difficulties in direct methods of measuring it and also the limited measurement points. thus, the efficien...

Mineral resource estimation involves the determination of grade and tonnage a mineral deposit based on its geological characteristics using various methods. Conventional methods, such as geometric geostatistical techniques, remain most widely used methods for estimation. However, recent advances in computer algorithms have allowed researchers to explore potential machine learning techniques Thi...

Transforming complex multivariate geological data to be multivariate Gaussian is an important and challenging problem in geostatistics. A variety of transforms are available to accomplish this goal, but may struggle with data sets of high dimensional and sample sizes. Projection Pursuit Density Estimation (PPDE) is a well-established non-parametric method for estimating the joint PDF of multiva...

This study estimated hepatitis A risk areas in a region of Duque de Caxias, Rio de Janeiro State, Brazil. A cross-sectional study consisting of a hepatitis A serological survey and a household survey were conducted in 19 census tracts. Of these, 11 tracts were selected and 1,298 children from one to ten years of age were included in the study. Geostatistical techniques allowed modeling the spat...