Fuzzy inference systems for mineral prospectivity modeling-optimized using Monte Carlo simulations
This paper uses Monte Carlo simulations to estimate the parameters of rule-based fuzzy inference systems (FISs) designed for mineral prospectivity modeling. The targeted process for the case study is gold mineralization in the Rajapalot project area in northern Finland. Mamdani-type FISs are develop...
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ftdoajarticles:oai:doaj.org/article:d6d94a7d62dd49fcac5306f5a459b60c 2023-05-15T17:42:33+02:00 Fuzzy inference systems for mineral prospectivity modeling-optimized using Monte Carlo simulations Bijal Chudasama 2022-01-01T00:00:00Z https://doi.org/10.1016/j.mex.2022.101629 https://doaj.org/article/d6d94a7d62dd49fcac5306f5a459b60c EN eng Elsevier http://www.sciencedirect.com/science/article/pii/S2215016122000140 https://doaj.org/toc/2215-0161 2215-0161 doi:10.1016/j.mex.2022.101629 https://doaj.org/article/d6d94a7d62dd49fcac5306f5a459b60c MethodsX, Vol 9, Iss , Pp 101629- (2022) Optimization of a Mamdani-type fuzzy inference systems using Monte Carlo simulations Science Q article 2022 ftdoajarticles https://doi.org/10.1016/j.mex.2022.101629 2022-12-30T19:36:27Z This paper uses Monte Carlo simulations to estimate the parameters of rule-based fuzzy inference systems (FISs) designed for mineral prospectivity modeling. The targeted process for the case study is gold mineralization in the Rajapalot project area in northern Finland. Mamdani-type FISs are developed and implemented for the predictive modeling of favorable structural settings and favorable chemical traps causing gold enrichment in host rocks from ore-bearing hydrothermal fluids. The parameters of the fuzzification functions control the output fuzzy membership values. Traditionally these parameters are chosen subjectively based on the expert's domain knowledge. This study uses drill core data statistics to define the distribution of the parameters. Subsequently, Monte Carlo simulations are used to simulate the corresponding fuzzy membership values and optimize the FISs. • Capturing the complexities of the multi-processes geodynamic systems and the possible interplay mineralization-related geological aspects using ‘If-Then’ rule-based fuzzy inference systems. • Implementation of Monte Carlo simulations for quantification of uncertainties related to a Mamdani-type FIS-based prospectivity modeling. • Reporting prospectivity modeling results at different confidence levels for informed decision making on selection of exploration targets. Article in Journal/Newspaper Northern Finland Directory of Open Access Journals: DOAJ Articles MethodsX 9 101629 |
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Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Optimization of a Mamdani-type fuzzy inference systems using Monte Carlo simulations Science Q |
spellingShingle |
Optimization of a Mamdani-type fuzzy inference systems using Monte Carlo simulations Science Q Bijal Chudasama Fuzzy inference systems for mineral prospectivity modeling-optimized using Monte Carlo simulations |
topic_facet |
Optimization of a Mamdani-type fuzzy inference systems using Monte Carlo simulations Science Q |
description |
This paper uses Monte Carlo simulations to estimate the parameters of rule-based fuzzy inference systems (FISs) designed for mineral prospectivity modeling. The targeted process for the case study is gold mineralization in the Rajapalot project area in northern Finland. Mamdani-type FISs are developed and implemented for the predictive modeling of favorable structural settings and favorable chemical traps causing gold enrichment in host rocks from ore-bearing hydrothermal fluids. The parameters of the fuzzification functions control the output fuzzy membership values. Traditionally these parameters are chosen subjectively based on the expert's domain knowledge. This study uses drill core data statistics to define the distribution of the parameters. Subsequently, Monte Carlo simulations are used to simulate the corresponding fuzzy membership values and optimize the FISs. • Capturing the complexities of the multi-processes geodynamic systems and the possible interplay mineralization-related geological aspects using ‘If-Then’ rule-based fuzzy inference systems. • Implementation of Monte Carlo simulations for quantification of uncertainties related to a Mamdani-type FIS-based prospectivity modeling. • Reporting prospectivity modeling results at different confidence levels for informed decision making on selection of exploration targets. |
format |
Article in Journal/Newspaper |
author |
Bijal Chudasama |
author_facet |
Bijal Chudasama |
author_sort |
Bijal Chudasama |
title |
Fuzzy inference systems for mineral prospectivity modeling-optimized using Monte Carlo simulations |
title_short |
Fuzzy inference systems for mineral prospectivity modeling-optimized using Monte Carlo simulations |
title_full |
Fuzzy inference systems for mineral prospectivity modeling-optimized using Monte Carlo simulations |
title_fullStr |
Fuzzy inference systems for mineral prospectivity modeling-optimized using Monte Carlo simulations |
title_full_unstemmed |
Fuzzy inference systems for mineral prospectivity modeling-optimized using Monte Carlo simulations |
title_sort |
fuzzy inference systems for mineral prospectivity modeling-optimized using monte carlo simulations |
publisher |
Elsevier |
publishDate |
2022 |
url |
https://doi.org/10.1016/j.mex.2022.101629 https://doaj.org/article/d6d94a7d62dd49fcac5306f5a459b60c |
genre |
Northern Finland |
genre_facet |
Northern Finland |
op_source |
MethodsX, Vol 9, Iss , Pp 101629- (2022) |
op_relation |
http://www.sciencedirect.com/science/article/pii/S2215016122000140 https://doaj.org/toc/2215-0161 2215-0161 doi:10.1016/j.mex.2022.101629 https://doaj.org/article/d6d94a7d62dd49fcac5306f5a459b60c |
op_doi |
https://doi.org/10.1016/j.mex.2022.101629 |
container_title |
MethodsX |
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9 |
container_start_page |
101629 |
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1766144425348562944 |