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Geophysics is an essential part of most modern mineral exploration programs for iron oxide copper-gold deposits. This paper reviews the important physical properties, which are the basis for the application of geophysical methods, and attempts to illustrate and summarise the ways they have been applied with data and images from selected deposits. Some comments are provided on their historical effectiveness and the role of these methods in an overall program, which must use all available data from geology, mineralogy, geochemistry and geophysics.
The mining towns in northern Canada are remote from good roads but they are surrounded by lakes. Aircraft, mounted on floats instead of wheels, serve as a practical means of transport in the forested wilderness. After the ice had melted from the lakes in the early summer of 1961, I found myself —a young Scot with a good recent degree in physics but minimum training for the task ahead—disembarking from a float-plane on the shore of a large lake in northern Manitoba, 80 kilometres from the nearest town where the mining company that employed me had its headquarters. My companions were five Cree Indians and a cook, and for the rest of the short northern summer my job would be to use geophysical equipment to search for potential sources of nickel in the geological structures buried beneath the forest floor. This was my initiation to the world of geophysical exploration. It convinced me to change from physics to a career as a geophysicist. In addition to teaching and laboratory research it involved annual fieldwork that took me into less developed areas of the world where interesting geological problems could be addressed.
Nowadays, many surficial mineral deposits are being mined out, leaving only deep-seated mineral deposits for feeding raw materials into the industry. Therefore techniques applied to mineral exploration need to be revisited for discovering new mineral resources, which may be located in harsh and remote regions. Over the past decades, remote sensing technology and geographic information system (GIS) techniques have been incorporated into several mineral exploration projects worldwide. This aim is to bridge the knowledge gap for the geospatial-based discovery of buried, covered, and blind mineral deposits. This book details the main aspects of the state-of-the-art remote sensing imagery, geochemical data, geophysical data, geological data, and geospatial toolbox required to explore ore deposits. It covers advances in remote sensing data processing algorithms, geochemical data analysis, geophysical data analysis, and machine learning algorithms in mineral exploration. It also presents approaches on recent remote sensing and GIS-based mineral prospectivity modeling, which offer a piece of excellent information to professional earth scientists, researchers, mineral exploration communities, and mining companies <...>
Geological data, notably geochemical data, often take the form of a regionalized composition. The concept of regionalized composition combines the concepts of composition and coregionalization. A composition, also known in the literature as a closed array (Chayes 1962), is a random vector whose components add up to a constant. A coregionalization is a set of two or more regionalized variables defined over the same spatial domain, which is modeled as a realization of a vector random function. Here the term regionalized composition is used both for the vector random function used to model a composition and for the realization that we can observe <...>
Mineral resource evaluation requires defining geological domains that differentiate the types of mineralogy, alteration and lithology. Usual practice is to consider the domain boundaries as hard, i.e. data from across the boundaries are disregarded when estimating the grades within a given domain. This practice may hinder the quality of the estimates when a significant spatial correlation of the grades exists across the domain boundaries.
Spatial statistics has developed rapidly during the last 30 years. We have seen an interesting progress both in theoretical developments and in practical studies. Some early applications were in mining, forestry, and hydrology. It seems to be honest to remark that the increasing availability of computer power and skillful computer software has stimulated the ability to solve increasingly complex problems.
This text intends to be a technical one. This means that techniques to solve identified problems will be presented. As the theory which serves as a basis for these techniques is very new, and relatively unfamiliar to the mineral industry, several chapters or sections will be devoted to it. These two ideas of a technique and a theory have been my guideline in preparing this course on the geostatistical estimation of mineral resources. The main target was to stay, as much as possible, close to the practical problems. This is the reason for the many examples which are intermeshed with the text; however, in many cases, staying t o o close t o a problem obscures the broader frame into which a question has to be asked before finding a correct answer. This is the reason for some theoretical digressions, which may seem to some as an attempt to try and make things look complicated. Certainly, in a particular mine, many problems can be solved without a total understanding of the complete theory. On the other hand, when one considers all the problems occurring in different mines, one cannot hope to solve them without having a good grasp, a synthetic view of the theory of regionalized variables as developed by G. Matheron in France, the most advanced developments of which have just been published in the Proceedings of a N.A.T.O. Advanced Study Institute (Guarascio, Huijbregts, David, 1976) <...>
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. <...>
Miners and geologists have always known that the value of minerals in a given volume of ore depends heavily on the position of the ore in the orebody and on the value of the ground surrounding it. Thus, traditional methods of ore reserve estimation have attempted to combine data, on the position of the sample with an intuitive notion of "area of influence" to produce usable results. Polygonal and triangular weighting, rectangular zones of influence, and inverse distance methods were all developed so that both characteristics—spatial position and value of surrounding ground-would be included in the estimation. However, there is no objective way to measure the reliability of these estimating techniques.
The natural resources on the earth seem to be randomly distributed but their variations over space and time are not all random. They exhibit a spatial correlation. This spatial correlation can be captured by geostatistics. Geostatistics deals with the analysis and modelling of geo-referenced data. The point observations are analyzed and interpolated to create spatial maps. For geostatistical interpolation, first the spatial correlation structures of the parameter of interest are quantified and then spatial interpolation is done using the quantified spatial correlation and optimal predictions at unobserved locations to create a map.
