Optimal exploration target zones

Abstract

This research describes a quantitative methodology for deriving optimal exploration target zones based on a probabilistic mineral prospectivity map. In order to arrive at out objective, we provide a plausible answer to the following question: "Which areas of high likelihood of mineral deposit occurrence are optimal exploration target zones for further surveying of undiscovered occurrences of mineral deposits of the type sought?" The methodology is demonstrated in the Rodalquilar mineral district in Spain. A subset of known occurrences of mineral deposits of the type sought were considered discovered and then used as training data, and a map of distances to faults/fractures and three band ratio images of hyperspectral data were used as layers of spatial evidence in weights-of-evidence (WofE) modeling of mineral prospectivity in the study area. This result in a mineral prospectivity map, which is based on a Bayesian probability framework to update the prior probability of mineral deposit occurrences of the type sought in every unit cell or pixel in a study area. A derived posterior probability map of mineral deposit occurrence showing non-violation of the conditional independence assumption and having the highest prediction rate was then input to an objective function in simulated annealing in order to derive a set of optimal exploration focal points. In order to determine the optimal exploration target zones from a given mineral prospectivity map, we adopt the following paradigm. In searching for target object(s) of interest, not only in regional- or district-scale mineral exploration but also in other types of 'search endeavors' at similar scales (i.e., in large areas), one intuitively defines at first instance a focal point according to a set of criteria and then draws a perimeter (i.e., a search radius) around such a focal point according to another set of criteria. The perimeter around the focal point is usually, but not always, circular within which to continue searching for the target object(s) of interest more intensively. Thus, with this intuitive paradigm, we used a WofE-derived posterior probability map in order to determine optimal exploration target zones in the following way. First, we used the prediction rate of the WofE-derived posterior probability map and the number of cross-validation deposits delineated correctly by the map in order to estimate a number of exploration focal points. For this purpose, we used the binomial distribution model. Second, we used the posterior probabilities in the WofE-derived map and the estimated number of exploration focal points as input data and as a control parameter, respectively, in order to derive the locations of optimal exploration focal points. An optimal exploration focal point is a pixel or location, at and around which there is high posterior probability of mineral deposit occurrences of the type sought. Buffering of each optimal exploration focal point, based on proximity analysis, results in optimal exploration target zones, many of which coincide spatially with at least one of the occurrences of mineral deposit of the type sought in the subset of cross-validation (i.e., presumed undiscovered) mineral deposits of the type sought. The results of the study show usefulness of the proposed methodology for objective delineation of optimal exploration target zones based on a probabilistic mineral prospectivity map.