Geological modelling is generally based on deterministic models, which provide a single representation of reality. Probabilistic modelling is more appropriate when quantifying or understanding the uncertainty associated with a parameter of interest as it considers several equally probable geological scenarios. The object of this study is to quantify the uncertainty in the estimation of the minerals in the Punta Alegre gypsum deposit, by applying a new method based on the simple normal equation geostatistical simulation technique. The Punta Alegre gypsum deposit is a sedimentary deposit of clastic origin, formed by the complex redeposition of salts, gypsum and other sediments. To carry out this research, 50 equiprobable scenarios were simulated, reproducing 1- overburden, 2- gypsum series (different types of gypsum) and 3-intercalated non-mineral lithologies (limestone and other rocks), in a network of nodes measuring 5 x 5 x 5 meters, using a training image, composites and prior probability maps as input data. As a result of scaling the previously simulated geological units, three-dimensional models of volume proportions and estimation error for gypsum were obtained for panels measuring 10 x 10 x 5 meters. The quantification of the uncertainty of the gypsum volume, determined by the root mean square error, established that the volume estimation error is small at a global scale (6.51%), given that there is no significant variation when comparing the deterministic model with the gypsum proportion model obtained from the 50 simulated scenarios. Conversely, at the local scale, there is a significant variation in gypsum volume of 42% in the 10 x 10 x 5 meter panels with a future impact on recoverable mining resources, given the uncertainty at a local scale, which will cause an increase in mining dilution due to the inclusion of non-mineral lithologies within the extracted mineral that will be sent to the processing plant. On the other hand, it will cause changes in the mining companys plan in areas where there are panels that were previously accounted for by the deterministic model as minerals and are not actually exploitable.
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Arias et al. (Mon,) studied this question.
synapsesocial.com/papers/68bb4def6d6d5674bcd01edb — DOI: https://doi.org/10.20944/preprints202509.0114.v1
José Arias
University of British Columbia
Alain Carballo
Hospital Oncológico Docente "Conrado Benítez García"
Elmidio Estévez
University of Pinar del Río
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