Operations Research - Operations Research and Regional Mineral Exploration

This paper surveys a few of the quantitative exploration models that might be of interest to an ex-plorationist seeking to apply methods of operations research to mineral exploration. A general development of each model is presented, and the model is evaluated relative to the exploration activity. Finally, a philosophy of exploration and mineral resources is presented, and a possible exploration strategy incorporating concepts of some of these models consistent with this philosophy is proposed. The high cost of conducting an effective exploration program has generated an increasing interest in methods of optimizing the allocation of the resources of a firm engaged in mineral exploration. As a step towards this, mathematical models have been formulated which are designed to quantify those variables of mineral exploration in such a manner that they can be integrated with all phases of the firm's operations so as to optimize its profit objective. QUANTITATIVE MODELS General: Exploration models express at least two concepts: mineral occurrence and effectiveness of search. In simplest terms, an exploration model might be formulated mathematically as follows: P(X) = F(X) . G(X), where P(X) = the unconditional probability of discovery of X deposits, F(X) = the probability of occurrence of X deposits, and G(X) = the probability of discovery of X deposits, conditional upon their existence in the search area. With these general models as a basis of reference, some of the specific exploration models are examined below. The Allais Model: DEVELOPMENT - M. Allaisl performed the first study in which a philosophy of exploration was formulated into a probability framework. His objective was to estimate the economic profitability of the exploration of the Algerian Sahara. Allais divided the Algerian Sahara into blocks of land (called cells) 10 km square and defined the random variable, X, as the number of mining districts per cell. Thus, F(X) would describe the probability of 1, 2, or n mining districts occurring within any cell of the search area. Allais reasoned that his best information as to the form of the distribution, F(X), was the number of mining districts per cell that had been found in well explored areas in the world, such as the Western States of the U.S. He found that the Poisson function produced a reasonably good fit to the distribution of districts in these explored areas: where? is a parameter, such as the average number of districts per cell. Thus, by determining the value