Mapping Mineral Prospectivity Using a Hybrid Genetic Algorithm–Support Vector Machine (GA–SVM) Model

Machine learning (ML) as a powerful data-driven method is widely used for mineral prospectivity mapping. This study employs a hybrid of the genetic algorithm (GA) and support vector machine (SVM) model to map prospective areas for Au deposits in Karamay, northwest China. In the proposed method, GA i...

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Autores principales:	Xishihui Du, Kefa Zhou, Yao Cui, Jinlin Wang, Shuguang Zhou
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	support vector machine genetic algorithm mineral prospectivity mapping Au deposits Geography (General) G1-922
Acceso en línea:	https://doaj.org/article/f9114c4c324045769e952e6f0bf9f753
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id	oai:doaj.org-article:f9114c4c324045769e952e6f0bf9f753
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spelling	oai:doaj.org-article:f9114c4c324045769e952e6f0bf9f7532021-11-25T17:53:07ZMapping Mineral Prospectivity Using a Hybrid Genetic Algorithm–Support Vector Machine (GA–SVM) Model10.3390/ijgi101107662220-9964https://doaj.org/article/f9114c4c324045769e952e6f0bf9f7532021-11-01T00:00:00Zhttps://www.mdpi.com/2220-9964/10/11/766https://doaj.org/toc/2220-9964Machine learning (ML) as a powerful data-driven method is widely used for mineral prospectivity mapping. This study employs a hybrid of the genetic algorithm (GA) and support vector machine (SVM) model to map prospective areas for Au deposits in Karamay, northwest China. In the proposed method, GA is used as an adaptive optimization search method to optimize the SVM parameters that result in the best fitness. After obtaining evidence layers from geological and geochemical data, GA–SVM models trained using different training datasets were applied to discriminate between prospective and non-prospective areas for Au deposits, and to produce prospectivity maps for mineral exploration. The F1 score and spatial efficiency of classification were calculated to objectively evaluate the performance of each prospectivity model. The best model predicted 95.83% of the known Au deposits within prospective areas, occupying 35.68% of the study area. The results demonstrate the effectiveness of the GA–SVM model as a tool for mapping mineral prospectivity.Xishihui DuKefa ZhouYao CuiJinlin WangShuguang ZhouMDPI AGarticlesupport vector machinegenetic algorithmmineral prospectivity mappingAu depositsGeography (General)G1-922ENISPRS International Journal of Geo-Information, Vol 10, Iss 766, p 766 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	support vector machine genetic algorithm mineral prospectivity mapping Au deposits Geography (General) G1-922
spellingShingle	support vector machine genetic algorithm mineral prospectivity mapping Au deposits Geography (General) G1-922 Xishihui Du Kefa Zhou Yao Cui Jinlin Wang Shuguang Zhou Mapping Mineral Prospectivity Using a Hybrid Genetic Algorithm–Support Vector Machine (GA–SVM) Model
description	Machine learning (ML) as a powerful data-driven method is widely used for mineral prospectivity mapping. This study employs a hybrid of the genetic algorithm (GA) and support vector machine (SVM) model to map prospective areas for Au deposits in Karamay, northwest China. In the proposed method, GA is used as an adaptive optimization search method to optimize the SVM parameters that result in the best fitness. After obtaining evidence layers from geological and geochemical data, GA–SVM models trained using different training datasets were applied to discriminate between prospective and non-prospective areas for Au deposits, and to produce prospectivity maps for mineral exploration. The F1 score and spatial efficiency of classification were calculated to objectively evaluate the performance of each prospectivity model. The best model predicted 95.83% of the known Au deposits within prospective areas, occupying 35.68% of the study area. The results demonstrate the effectiveness of the GA–SVM model as a tool for mapping mineral prospectivity.
format	article
author	Xishihui Du Kefa Zhou Yao Cui Jinlin Wang Shuguang Zhou
author_facet	Xishihui Du Kefa Zhou Yao Cui Jinlin Wang Shuguang Zhou
author_sort	Xishihui Du
title	Mapping Mineral Prospectivity Using a Hybrid Genetic Algorithm–Support Vector Machine (GA–SVM) Model
title_short	Mapping Mineral Prospectivity Using a Hybrid Genetic Algorithm–Support Vector Machine (GA–SVM) Model
title_full	Mapping Mineral Prospectivity Using a Hybrid Genetic Algorithm–Support Vector Machine (GA–SVM) Model
title_fullStr	Mapping Mineral Prospectivity Using a Hybrid Genetic Algorithm–Support Vector Machine (GA–SVM) Model
title_full_unstemmed	Mapping Mineral Prospectivity Using a Hybrid Genetic Algorithm–Support Vector Machine (GA–SVM) Model
title_sort	mapping mineral prospectivity using a hybrid genetic algorithm–support vector machine (ga–svm) model
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/f9114c4c324045769e952e6f0bf9f753
work_keys_str_mv	AT xishihuidu mappingmineralprospectivityusingahybridgeneticalgorithmsupportvectormachinegasvmmodel AT kefazhou mappingmineralprospectivityusingahybridgeneticalgorithmsupportvectormachinegasvmmodel AT yaocui mappingmineralprospectivityusingahybridgeneticalgorithmsupportvectormachinegasvmmodel AT jinlinwang mappingmineralprospectivityusingahybridgeneticalgorithmsupportvectormachinegasvmmodel AT shuguangzhou mappingmineralprospectivityusingahybridgeneticalgorithmsupportvectormachinegasvmmodel
_version_	1718411899858583552

Mapping Mineral Prospectivity Using a Hybrid Genetic Algorithm–Support Vector Machine (GA–SVM) Model

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