Fuzzy logic : Identifying areas for mineral development (original) (raw)
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Evaluation of the Performance of Fuzzy Logic Applied in Spatial Analysis for Mineral Prospecting
2000
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
GIS modeling using fuzzy logic approach in mineral prospecting based on geophysical data
The case study of geophysical prospective modelling for High Sulphidation Epithermal (HSE) Au deposit was taken over the Seruyung gold mine, located in the Nunukan Regency, North Kalimantan which is close to the Equator. Brown field exploration drilling is important to improve the mine life by adding the resources. Predicting realistic drill target is important to reduce the drill cost risk and loss opportunity to find the hidden target. The geophysical exploration method is the superior over the project area due to the dense of vegetation and thick soil so very limited geological outcrops. Due to between the ore body and host rock have contrast physical contents, geophysical survey could be used to assist to map the physical property bellow surface. Integrating the existing several layers is better than using a single layer of geophysical anomaly to predict the extension of ore body. Simplified Fuzzy method for mineral prospecting was implemented in this modelling and returned about 90% confident to delineate the existing ore body. MS Excel program was used to simplify the rules and the parameters of the modelling process.
2001
An application of the theory of fuzzy sets to the mapping of gold mineralization potential in the Baguio gold mining district of the Philippines is described. Proximity to geological features is translated into fuzzy membership functions based upon qualitative and quantitative knowledge of spatial associations between known gold occurrences and geological features in the area. Fuzzy sets of favorable distances to geological features and favorable lithologic formations are combined using fuzzy logic as the inference engine. The data capture, map operations, and spatial data analyses are carried out using a geographic information system. The fuzzy predictive maps delineate at least 68% of the known gold occurrences that are used to generate the model. The fuzzy predictive maps delineate at least 76% of the "unknown" gold occurrences that are not used to generate the model. The results are highly comparable with the results of previous stream-sediment geochemical survey in the area. The results demonstrate the usefulness of a geologically constrained fuzzy set approach to map mineral potential and to redirect surficial exploration work in the search for yet undiscovered gold mineralization in the mining district. The method described is applicable to other mining districts elsewhere.
Preparing Mineral Potential Map Using Fuzzy Logic in Gis Environment
In vector model, the boundary between features is stored and shown categorically in an absolute way, in other words Membership or non-membership of a point in each polygon is known which can be 0 or 1. For showing real world and its projection on map, because of the uncertainty on the border of the boundary between phenomena and spatial data, there is some uncertainty and vagueness in drawing and depicting features. Because of the uncertainty in membership and non-membership amount of each point of factorial map according to the distance from spatial features, fuzzy logic would expose and show the existence vague between phenomena and features, and thereafter a closer result to the real world. So using fuzzy logic could be useful and workable. Attempt of the present study is tend to describe the modelling and storage of real world on digitised map by fuzzy logic, and eventually the approach for maps' integration in GIS by fuzzy logic with application test is presented. In our research three fuzzy operators proposed and using these operators, factor maps are integrated in case study. The result of using these operators comparing with conventional fuzzy operators shows that three proposed fuzzy operators have better result or have equal values with conventional fuzzy operators. In the present work as a case study, mineral potential map of porphyry copper ore of Rigan Bam in Iran is prepared by using of fuzzy logic.
Data-driven fuzzy analysis in quantitative mineral resource assessment
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.
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.
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.
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.