Data-driven fuzzy analysis in quantitative mineral resource assessment (original) (raw)
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2009
This article looks at the application of fuzzy logic set theory in GIS to identify potential areas for mineral development. Arc-SDM (Spatial Data Modeller) was used to assign fuzzy membership values to the selected criteria and calculate a combined output surface indicating the potential of areas for gold mineral development based on fuzzy set membership. Arc-SDM is a software extension for ArcMap that provides additional geo-processing and modeling functionality, including fuzzy logic tools for geological and mineral applications1.
A Hybrid Fuzzy Weights-of-Evidence Model for Mineral Potential Mapping
Nonrenewable Resources, 2006
This paper describes a hybrid fuzzy weights-of-evidence (WofE) model for mineral potential mapping that generates fuzzy predictor patterns based on (a) knowledge-based fuzzy membership values and (b) data-based conditional probabilities. The fuzzy membership values are calculated using a knowledge-driven logistic membership function, which provides a framework for treating systemic uncertainty and also facilitates the use of multiclass predictor maps in the modeling procedure. The fuzzy predictor patterns are combined using Bayes’ rule in a log-linear form (under an assumption of conditional independence) to update the prior probability of target deposit-type occurrence in every unique combination of predictor patterns. The hybrid fuzzy WofE model is applied to a regional-scale mapping of base-metal deposit potential in the south-central part of the Aravalli metallogenic province (western India). The output map of fuzzy posterior probabilities of base-metal deposit occurrence is classified subsequently to delineate zones with high-favorability, moderate favorability, and low-favorability for occurrence of base-metal deposits. An analysis of the favorability map indicates (a) significant improvement of probability of base-metal deposit occurrence in the high-favorability and moderate-favorability zones and (b) significant deterioration of probability of base-metal deposit occurrence in the low-favorability zones. The results demonstrate usefulness of the hybrid fuzzy WofE model in representation and in integration of evidential features to map relative potential for mineral deposit occurrence.
Unsupervised clustering and empirical fuzzy memberships for mineral prospectivity modelling
Ore Geology Reviews, 2019
We propose to increase the role of empirical methods in mineral prospectivity modelling for two reasons: 1) to make use of data more effectively and 2) to decrease the effect of subjectivity included in expert interpretation. We present two approaches for using known mineral occurrences to define the relationship between observed or measured geoscientific parameters and the occurrence of mineralizations. In the first approach, we define the fuzzy memberships of each geoscientific parameter separately for fuzzy logic modelling. Our approach proves to be highly useful for investigating the quality of the data in addition to defining the membership transformation functions. In our test case, the data are somewhat scattered due to the inherent variability of ore-forming environments, and manual evaluation was required to guide the computations. For the second approach, we present a technique for delineating non-prospective regions to be able to focus more detailed prospectivity modelling to potentially prospective regions. Our study not only highlights the advantages of using computational methods in prospectivity modelling, but also emphasizes the important role of geological expertise in the modelling process.
Knowledge-Driven and Data-Driven Fuzzy Models for Predictive Mineral Potential Mapping
Nonrenewable Resources, 2003
In this paper, we describe new fuzzy models for predictive mineral potential mapping: (1) a knowledge-driven fuzzy model that uses a logistic membership function for deriving fuzzy membership values of input evidential maps and (2) a data-driven model, which uses a piecewise linear function based on quantified spatial associations between a set of evidential evidence features and a set of known mineral deposits for deriving fuzzy membership values of input evidential maps. We also describe a graphical defuzzification procedure for the interpretation of output fuzzy favorability maps. The models are demonstrated for mapping base metal deposit potential in an area in the south-central part of the Aravalli metallogenic province in the state of Rajasthan, western India. The data-driven and knowledge-driven models described in this paper predict potentially mineralized zones, which occupy less than 10% of the study area and contain at least 83% of the “model” and “validation” base metal deposits. A cross-validation of the favorability map derived from using one of the models with the favorability map derived from using the other model indicates a remarkable similarity in their results. Both models therefore are useful for predicting favorable zones to guide further exploration work.
Archives of Mining Sciences, 2019
The basis for a mineral deposit delimitation is a qualitative and quantitative assessment of deposit parameters, qualifying a deposit as an economically valuable object. A conventional approach to the mineral deposit recognition and a deposit detailed parameters qualification in the initial stages of development work in the KGHM were presented in the paper. The goals of such recognition were defined, which through a gradual detailed expansion, resulting from the information inflow, allows for the construction of a more complete decision-making model. The description of the deposit parameters proposed in the article in the context of fuzzy logic, enables a presentation of imprecise statements and data, which may be a complement to a traditional description. Selected non-adjustable and adjustable s-norm and t-norm operators were demonstrated. Operators effects were tested for selected ore quality parameters (copper content and deposit thickness) by constructing adequate membership fun...
Geostandards and Geoanalytical Research, 2011
For mineral resource assessment, techniques based on fuzzy logic are attractive because they are capable of incorporating uncertainty associated with measured variables and can also quantify the uncertainty of the estimated grade, tonnage etc. The fuzzy grade estimation model is independent of the distribution of data, avoiding assumptions and constraints made during advanced geostatistical simulation, e.g., the turning bands method. Initially, fuzzy modelling classifies the data using all the component variables in the data set. We adopt a novel approach by taking into account the spatial irregularity of mineralisation patterns using the Gustafson-Kessel classification algorithm. The uncertainty at the point of estimation was derived through antecedent memberships in the input space (i.e., spatial coordinates) and transformed onto the output space (i.e., grades) through consequent membership at the point of estimation. Rather than probabilistic confidence intervals, this uncertainty was expressed in terms of fuzzy memberships, which indicated the occurrence of mixtures of different mineralogical phases at the point of estimation. Data from different sources (other than grades) could also be utilised during estimation. Application of the proposed technique on a real data set gave results that were comparable to those obtained from a turning bands simulation.
Mineral Prospectivity Mapping by Fuzzy Logic Data Integration, Kajan Area in Central Iran
Kajan area is located in east of Isfahan, within the Urmia-Dokhtar volcanic belt. The volcanic rocks of the area are mostly associated with the Tertiary volcanic activities. The current study is carried out to identify new promising targets for regional exploration. Multiple data sources (e.g., stream sediment geochemical data, magnetic surveys, faults, geological and satellite data) are processed and then integrated by using Fuzzy Logic modeling to produce a final favorability map for regional copper exploration in the Kajan area. Introduction Predictive prospectivity mapping is used to define favorable areas for mineral exploration (Rasekh et al, 2013). This method can be applied in various scales from global to local scale exploration targeting. Definition of the exploration model is based on mineralization model. This gives the framework for initializing a predictive mineralization targeting. In this research we first processed different exploration data set such as geological map at the scale of 1:1000000, stream sediment geochemistry, airborne magnetics, structural map and Aster satellite data to identify special proxies related to copper mineralization. Then the results were integrated by fuzzy logic data integration method to create a final potential map for regional copper exploration. Figure 1 shows the location map of the Kajan area at the Urumiye-Dokhtar volcanic belt and also the 1:1000000 geological map of the area.
Evaluation of the Performance of Fuzzy Logic Applied in Spatial Analysis for Mineral Prospecting
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Fuzzy logic, decision making procedure (AHP), and conditional probability were evaluated on the spatial analysis of geological data, to address potential areas for radioactive mineral occurrences in the Poços de Caldas Plateau ( ≅ 750 Km 2 ). Spatial inference techniques were applied controlled by a prospecting model based on diagnostic criteria, represented by favorable lithology, structures features and gamma-ray