Fuzzy sets, uncertainty, and information
Fuzzy sets, uncertainty, and information
Canonical favorability model for data integration and mineral potential mapping
Computers & Geosciences
Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
Selective mining of multiple-layer lignite deposits. A fuzzy approach
Expert Systems with Applications: An International Journal
Editorial: Geocomputation of mineral exploration targets
Computers & Geosciences
A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation
Computers & Geosciences
| Hi-index | 0.00 |
The integration of geo-information from multiple sources and of diverse nature in developing mineral favourability indexes (MFIs) is a well-known problem in mineral exploration and mineral resource assessment. Fuzzy set theory provides a convenient framework to combine and analyse qualitative and quantitative data independently of their source or characteristics.A novel, data-driven formulation for calculating MFIs based on fuzzy analysis is developed in this paper. Different geo-variables are considered fuzzy sets and their appropriate membership functions are defined and modelled. A new weighted average-type aggregation operator is then introduced to generate a new fuzzy set representing mineral favourability. The membership grades of the new fuzzy set are considered as the MFI. The weights for the aggregation operation combine the individual membership functions of the geo-variables, and are derived using information from training areas and L1 regression.The technique is demonstrated in a case study of skarn tin deposits and is used to integrate geological, geochemical and magnetic data. The study area covers a total of 22.5 km2 and is divided into 349 cells, which include nine control cells. Nine geo-variables are considered in this study. Depending on the nature of the various geo-variables, four different types of membership functions are used to model the fuzzy membership of the geo-variables involved.