Mineral Prospectivity Mapping by Fuzzy Logic Data Integration , Kajan Area in Central Iran (original) (raw)
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Minerals
Analyzing and fusing information layers of exploratory parameters is a crucial stride for increasing the accuracy of pinpointing mineral potential zones in the reconnaissance stage of mineral exploration. Remote sensing, geophysical, geochemical, and geology data were analyzed and fused for identify metallic mineralization in the Kodegan-Basiran region (East Iran). Landsat 7 Enhanced Thematic Mapper Plus (ETM+), aeromagnetic data, geological data, and geochemical stream sediment samples were utilized. The study area contains some copper indices and mines. Thus, the main focus of this study was identifying the zones with high potential for metallic copper mineralization. A two-stage methodology was implemented in this study: First, extraction of the exploratory parameters related to metallic mineralization and second is data fusion by the hybrid fuzzy-analytic hierarchy process (Fuzzy-AHP) method. Hydrothermal alterations and iron oxides in the area were mapped by applying the optimu...
Applied Geomatics, 2013
In this paper, we describe fuzzy models for predictive porphyry Cu potential mapping: (1) a knowledgedriven 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 piece-wise 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. The mineral favorability maps for porphyry Cu exploration were produced in a geographic information systems environment and took into account three sources of data and information: (1) satellite images; (2) a geochemical survey; (3) geo-structural mapping. These data and information were integrated through a conceptual model developed for porphyry Cu mines and occurrences in the studied region. Both favorability maps highlighted the known porphyry Cu occurrences and validated the approach, but the data-driven method shows better results than the knowledge-driven method.
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.
Fuzzy logic : Identifying areas for mineral development
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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.
The aim of this study is prospecting of iron and manganese in the Balvard 1:100000 sheet which is situated in Sanandaj -Sirjan structural zone utilizing Index Overlay and Fuzzy Logic methods in the GIS. In this study, the layers for integration, alterations, geological, geophysical, geochemical and structural data based on stream sediments, airborne magnetometeric and remote sensing studies. Based on results obtained by both of methods, Fe and Mn prospects exist in the NE and northern parts of the area. The prospect areas derived via the Fuzzy Logic method are larger than those of gained from the Index Overlay method because the method used in the range from 0 to 1 value.
A fuzzy-based prognosis of ore mineralization potentials in Ramand region (Qazvin province)
Journal of Mining and Environment, 2017
The Ramand region is a part of the magmatic belt in Urmieh-Dokhtar structural zone in Iran, located in the SW of BuinـZahra. This area mainly consists of felsic extrusions such as rhyolites and rhyodacites. Argillic alterations with occurrences of mineralized silica veins are abundant in most of the volcanic units. In this research work, we used the GIS facilities for modeling the Ramand geo-spatial databases according to the Fuzzy logic algorithms. The main phase of mineralization occurred in the altered regions and is located near the cross cut fault systems. Therefore, the main criteria for integration were the geological, structural, geophysical, and remotely sensed (Landsat7, ETM+) layers. Also we used a contoured aeromagnetic map for revealing and weighting lineaments. By the Fuzzy techniques applied, all the evidential themes were integrated to prognosis of ore mineralization potentials based on γ = 0.75. As a result, the hydrothermal alterations and their relevant post-magma...
Earth Science Informatics, 2014
The study area is located~50 km in the north of Tehran capital city, Iran, and is a part of central Alborz Mountain. The intrusive bodies aged post Eocene have intruded in the Eocene volcanic units causing hydrothermal alterations in these units. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images were used to map hydrothermal alteration zones. The propylitic, phyllic and argillic alteration and iron oxide minerals identified using Spectral Angle Mapper (SAM) method. Structural lineaments were extracted from ASTER images by applying automatic lineament extraction processes and visual interpretations. An exploration model was considered based on previous studies, and appropriate evidence maps were generated, weighted and reclassified. Ore Forming Potential (OFP) map was generated by applying Fuzzy SUM operator on alteration and Pb, Cu, Ag, and Au geochemical anomaly maps. Finally, Host rock, geological structures and OFP were combined using Fuzzy Gamma operator (γ) to produce mineral prospectivity map. Eventually, the conceptual model discussed here, fairly demonstrated the known hydrothermal gold deposits in the study area and could be a source for future detailed explorations.
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.
Open Journal of Geology, 2022
Iran is located on a silver, lead, and zinc belt and according to the latest studies holds 11 million tons of lead, zinc, and silver stones which constitute 4 percent of global resources. Considering that mineral materials are explored in an uncertain space, exploration investment risk is an inseparable part of these activities. The important fact is to minimize the effect of this undesired factor in exploration. To achieve this, it is required that exploration activities and withdrawals are performed in a certain framework in which risk minimization is considered. Using mineral potential modelling for determining promising zones which should be taken into consideration in more detailed stages could make achieving the purpose possibly. This work is aimed at applying fuzzy neural network and TOPSIS methods simultaneously in order to explore zinc and lead resources. In this article, geological, telemetry, geophysics, and geochemistry data is integrated using fuzzy-neural network (neuro fuzzy) and using TOPSIS method rating for lead and zinc ore deposit potential mapping in Isfahan-Khomein strip which has been introduced as one of zinc and leads mineral scopes in Iran. This area which is composed of several zinc and lead ore deposits has been considered as the target area. Fuzzy integration results of zinc and lead mineralization witness layers confirm the relatively high potential of lead and zinc mineralization in this region having a northwest-southeast trend and involving more than 90 percent of the known indices and ore deposits of the region. In this research, it was shown that the results of TOPSIS-Neuro-Fuzzy integrated model
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