A Hybrid Neuro-Fuzzy Model for Mineral Potential Mapping (original) (raw)
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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.
Nonrenewable Resources, 2003
This paper describes a GIS-based application of a radial basis functional link net (RBFLN) to map the potential of SEDEX-type base metal deposits in a study area in the Aravalli metallogenic province (western India). Available public domain geodata of the study area were processed to generate evidential maps, which subsequently were encoded and combined to derive a set of input feature vectors. A subset of feature vectors with known targets (i.e., either known mineralized or known barren locations) was extracted and divided into (a) a training data set and (b) a validation data set. A series of RBFLNs were trained to determine the network architecture and estimate parameters that mapped the maximum number of validation vectors correctly to their respective targets. The trained RBFLN that gave the best performance for the validation data set was used for processing all feature vectors. The output for each feature vector is a predictive value between 1 and 0, indicating the extent to which a feature vector belongs to either the mineralized or the barren class. These values were mapped to generate a predictive classification map, which was reclassified into a favorability map showing zones with high, moderate and low favorability for SEDEX-type base metal deposits in the study area. The method demarcates successfully high favorability zones, which occupy 6% of the study area and contain 94% of the known base metal deposits.
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.
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.
Natural Resources Research, 2010
The aim of this study is to analyze hydrothermal gold-silver mineral deposits potential in the Taebaeksan mineralized district, Korea, using an artificial neural network (ANN) and a geographic information system (GIS) environment. A spatial database considering 46 Au and Ag deposits, geophysical, geological, and geochemical data was constructed for the study area using the GIS. The geospatial factors were used with the ANN to analyze mineral potential. The Au and Ag mineral deposits were randomly divided into a training set (70%) to analyze mineral potential using ANN and a test set (30%) to validate predicted potential map. Four different training datasets determined from likelihood ratio and weight of evidence models were applied to analyze and validate the effect of training. Then, the mineral potential index (MPI) was calculated using the trained back-propagation weights, and mineral potential maps (MPMs) were constructed from GIS data for the four training cases. The MPMs were then validated by comparison with the test mineral occurrences. The validation results gave respective accuracies of 73. 06, 73.52, 70.11, and 73.10% for the training cases. The comparison results of some training cases showed less sensitive to training data from likelihood ratio than weight of evidence. Overall, the training cases selected from 10% area with low and high index value of MPM L and MPM W gave higher accuracy (73.52 and 73.10%) for MPMs than those (73.06 and 70.11%, respectively) from known deposits and 10% area with low index value of MPI L and MPI W .
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.
2013
The main problem associated with the traditional approach to image classification for the mapping of hydrothermal alteration is that materials not associated with hydrothermal alteration may be erroneously classified as hydrothermally altered due to the similar spectral properties of altered and unaltered minerals. The major objective of this paper is to investigate the potential of a neuro-fuzzy system in overcoming this problem. The proposed system is applied to the northwestern part of the Kerman Cenozoic Magmatic Arc (KCMA), which hosts many areas of porphyry and vein-type copper mineralization. A software program based on an Adaptive Neuro-Fuzzy Inference System (ANFIS) was developed using the MATLAB ANFIS toolbox. The ANFIS program was used to classify Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) data based on the spectral properties of altered and unaltered rocks. The ANFIS result was then compared with other classified images based on artificial ne...
Computers and Geotechnics, 2011
The rock engineering classification system is based on six parameters defined by Bieniawski [5] , who employed parallel sets of linguistic and numerical criteria that were acknowledged to influence the behaviour of rock masses and the stability of rock structures. Consequently, experts frequently relate rock joints and discontinuities as well as ground water conditions in linguistic terms, with rough calculations. Recently, intelligence system approaches such as artificial neural network (ANN) and neuro-fuzzy methods have been used successfully for time series modelling. Using neuro-fuzzy approaches, which enable the information that is stored in trained networks to be expressed in the form of a fuzzy rule base, would help to overcome this issue. This paper presents the results of a study of the application of neuro-fuzzy methods to predict rock mass rating. We note that the proposed weights technique was applied in this process. We show that neuro-fuzzy methods give better predicti...
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
Heavy minerals mapping using Fuzzy method
The aim of the present study is to develop a knowledge-driven expert system for mapping the potential heavy mineral placer deposits along the coast using integrated Geographical Information System (GIS) and fuzzy logic techniques. The study has been carried out in Kalaigananpuram coastal stretch, southeast coast of India, where high-quality heavy mineral placers are deposited naturally. A total of six transects was laid perpendicular to the coastline, in which 36 samples per transect of 2 m interval per month was taken for analysis. Standard techniques were adopted for evaluating the beach profile, grain size, and heavy mineral weight percent. Moreover, the statistical results of these parameters were considered for mapping the heavy mineral placer potential zone in the study area. A fuzzy-based expert system model was developed through ArcGIS model builder to map the mineral potential zone. In this study, three evidential layers were fuzzified using the linear membership function. These evidential layers were further ranked based on expert knowledge from placer minerals exploration researchers. The analyzed results reveal that heavy mineral placer deposits are observed in the berm and high-tide region of the study area. The annual average weight percent of heavy mineral placer deposits in the study area range from 40 % to 60 %. The extracted heavy mineral placer potential maps can be used for sustainable mining and surveying purposes.