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Uncertainty is a general term expressing lack of certainty and of precision in describing a geological object, a geological feature or a geological process. The mineral resources prediction and assessment (MRPA) has high uncertainty due to the complexity and variability of geological objects, such as the diversity of mineral deposit types, the complexity of mineral deposit genesis, the implicitness of mineral deposit controlling factors and the non-unique understanding of exploration information, and so on. There are two types of mineral resources prediction and assessment in terms of two key aims.
The first type of mineral resources location prediction(MRLP) is to predict where the mineral resources prospectivity are located in, in which the main sources of uncertainty are due to inherent natural variability and complexity, conceptual and model uncertainty, data error, and so on. The second type is mineral resources potential prediction (MRPP) to assess the quantity (tonnage) and quality (grades) of mineral resources, in which the main sources of uncertainties are due to most of mineral deposits being underground and the limited number of boreholes because of economic and other reasons. In this paper, the neural network integrated interval neutrosophic set (INS) was used to predict the location of mineral resources prospectivity. Two useful patterns are generated from the method. The first is the true membership to represent the degree of favorable prospectivty. The higher the value of true membership is, the more favorable the cells are.
The other pattern is the indeterminacy membership to represent the uncertainty due to vagueness of classification or not well understanding the mineralization. The higher the uncertainty is, the lower the confidence is. On the other hand, the fuzzy sets and fuzzy arithmetic were used to assess the quantity (tonnage) and quality (grades) of mineral resources. The uncertainties due to sampling error or estimation error were quantified by the overall error and relative error of fuzzy sets. The lower the overall error is, the lower the uncertainty is, and then the higher the confidence of assessment is.
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