In August 1882, the Morgan brothers recognised a mineral deposit, now known as the Mount Morgan Gold-Copper Deposit. The final production figures for the mine were 250 tonnes of gold and 360,000 tonnes of copper from 50 million tonnes of ore, making the average grades 4.99g/t gold and 0.72% copper.

The main developments during four decades of geostatistical applications on the Witwatersrand-type gold deposits are reviewed and critically compared with a view to highlighting the progress made to date. The basic techniques - from the preliminary borehole valuation of mining properties to the block estimating of ore reserves and foreseeable recoveries are analysed and the prospects for methodological improvements are discussed.

Since October 1975 and the first NATO ASl "Geostat - Roma 1975", there has not been an advanced workshop on geostatistics where experts from throughout the world could meet, teach and debate without the pressure of an ordinary professional conference. This second NATO ASI "Geostat - Tahoe 1983" was intended as a high-level teaching activity yet opened to all new ideas and contributions to the discipline of geostatistics. It was expected that the institute would bridge the gap since "Geostat - Roma 1975" and establish the state of the art of the discipline as of 1983 <...>

Although geologists have used statistical analysis in their research for many years, only recently has geostatistics, as a creative tool in the geosciences, received the attention it deserves. Even now many geologists are unaware of the tremendous power statistical methods offer. Today earth scientists can use summary statistics for large data bases, frequency distributions, sampling designs and problems, and applications of stochastic models and use them in dynamic ways for research and developm·ent.

Stochastic simulation has been suggested as a viable method for characterizing the uncertainty associated with the prediction of a nonlinear function of a spatially-varying parameter. Geostatistical simulation algorithms generate realizations of a random field with specified statistical and geostatistical properties. A nonlinear function (called a transfer function) is evaluated over each realization to obtain an uncertainty distribution of a system response that reflects the spatial variability and uncertainty in the parameter. Crucial management decisions, such as potential regulatory compliance of proposed nuclear waste facilities and optimal allocation of resources in environment al remediation, are based on the resulting system response uncertainty distribution. <...>

The paper presents some contemporary approaches to spatial environmental data analysis. The main topics are concentrated on the decision-oriented problems of environmental spatial data mining and modeling: valorization and representativity of data with the help of exploratory data analysis, spatial predictions, probabilistic and risk mapping, development and application of conditional stochastic simulation models. The innovative part of the paper presents integrated/hybrid model—machine learning (ML) residuals sequential simulations—MLRSS.

A requirement for geostatistical prediction is estimation of the variogram from the data. Often low sample size is a major impediment to elucidating a variogram even for a highly autocorrelated spatial process. This paper presents a methodology for improving variogram estimation when samples exist from multiple years or regions sharing a similar process for generating spatial autocorrelation.

Although it is generally understood that the Antarctic Ice Sheet plays a critical role in the changing global system, there is to date still a lack of generally available information on the subject. Climatic change and the role of the polar areas are often discussed in the media.