No description available.

No description available.

No description available.

Sampling is an error generating process and these errors should be reduced to a minimum if an accurate ore reserve estimation is to be made from the sample values. Error in sampling can arise from the sampling procedure as well as where and how each sample is taken from the deposit . Sampling procedure involves sample collection, sample reduction and analysis, and the error from each of these three stages has an equal influence on the total error of the process. Error due to sampling procedure should be identified and eliminated at an early stage in the evaluation programme. An ore deposit should be subdivided into sampling strata along geological boundaries, and once these boundaries have been established they should be adhered to for the evaluation programme. The sampling of each stratum depends on the small-scale structures in which the grade is distributed, and this distribution in relation to sample size controls sample variance, sample bias and the volume of influence of each sample. Cluster sampling can be used where an impractically large sample is necessary to reduce sample variance or increase the volume of influence of samples. Sample bias can be reduced by composing a large number of small samples . Sampling patterns should be designed with reference to the volumes of influence of samples, and in favourable geology, geostatistical or statistical techniques can be used to predict the precision of an ore reserve estimation 1n terms of the number of samples taken. Different are deposits have different sampling characteristics and problems which can be directly related to the geology of the mineralization. If geology is disregarded when sampling an are deposit, an evaluation programme cannot claim to give an accurate estimate of the ore reserves .

Call number: LD2668 .T4 1964 H67 / Master of Science

The design of a computerized system for the estimation of uranium potential supply in the United States constitutes the primary objective of this dissertation. Once completed, this system performs for various levels of economic variables, such as prices, the estimation of potential uranium supply without requiring the appraisal by geologists, area by area, of undiscovered uranium endowment. The main components that form the system are explicit models of endowment, exploration, and production. These component models are derived from engineering and geological data, and together, they comprise the system. This system is unique in that it links physical attributes of endowment to time series of price and production. This linkage is made by simulating the activities of the U.S. uranium industry, activities (exploration, mine development, and production) that are involved in the transformation of endowment to potential supply. Uranium endowment is first generated by employing a crustal abundance model; a data file containing characteristics (tonnage, grade, depth, intra-deposit grade variation) of the discrete deposits that comprise the endowment is established by this model. An exploration model relates discoveries to exploration effect and deposit characteristics. Discovery yield for a given effort is linked to the relative "discoverability" of the deposits of the endowment as well as to the total exploration effort. An economic evaluation is performed on each discovery to determine whether or not the deposit can be developed and produced, given the stated level of the economic variables. The system then determines the magnitude of potential supply that could be forthcoming from all discoverable and exploitable deposits for the stated economic circumstances. Initially, the parameters of the system must be estimated. The approach employed for this estimation makes use of the time series information on uranium exploration and production activities. In essence, the system is used to simulate the past history of the U.S. uranium industry (period 1948-1978) and to generate industry statistics for these activities; the parameters selected are those values that cause the system to yield a time series that matches closely that which actually occurred.

The competition in the world market for iron ore has increased lately. Therefore, an improved method of estimating the ore quality in small working areas has become an attractive cost-cutting strategy in short-term mine plans. Estimated grades of different working areas of a mine form the basis of any short-term mine plan. The generally sparse exploration data obtained during the development phase is not enough to accurately estimate the grades of small working areas. Therefore, additional sample information is often required in any operating mine. The findings of this case study show that better utilization of all available exploration information at this mine would improve estimation of small working areas even without additional face samples. Through the use of kriging variance, this study also determined the optimum face sampling grid, whose spacing turned out to be approximately 100 meters as compared to 50 meters in use today. (Abstract shortened with permission of author.)

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This study introduces a new methodology referred to as geoinformation synthesis for multivariate evaluation of mineral resources and integration of diverse geoscience data. The most critical component is the development of the notion of intrinsic samples and the methods for their delineation. Intrinsic samples replace grid cells which are conventionally employed as the basic information reference. Grid cell sampling has imposed several serious limitations on the geoscience and genetic information that can be objectively related to mineral endowment. Methods based upon intrinsic samples moderate to a certain extent these problems and bring the critical genetic information into the geoscience information system which forms the basis for the quantitative evaluation of mineral resources. The second major component in this new methodology is the integration of factors describing exploration effects with other geodata and mineral endowment estimation; this combination effectively reduces the possibilities of biases in the estimates of mineral endowment and recoverable resources due to the incomplete knowledge on the control area and imperfect analogy with the study areas. The third component is the use in the qualitative models of synthesized geoinformation, which is considerably enhanced, instead of using directly the original measurements (geodata). Several multivariate techniques are proposed and employed for synthesis of diverse information and estimation of mineral endowment, including a priori weighted multivariate criterion, optimum discretization, coherency analysis, multidimensional scaling method (p(ijk), filtering analysis, and geochemical transportation models. These methods were developed, tested, and demonstrated on an actual case study of the epithermal gold-silver deposits in the Walker Lake quadrangle of Nevada and California using various data sets available for this region: geochemical, structural, gravity and magnetic, lithology, and alteration. Finally, the estimation of endowment in terms of epithermal gold-silver mineral occurrences is given for some selected intrinsic samples or information zones identified in the Walker Lake region.

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