Gold potential mapping using bivariate and multivariate statistical models in GIS at Gua Musang, Malaysia

Throughout the history of humanity, gold remains as the most desired metals. Due to its small occurrences in the earth’s crust, this precious metal acquisition is very difficult. In Malaysia, gold potential map have been generated using conventional methods and are inadequate and lacking the abili...

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Main Author:	Yusoff, Suhaimizi
Format:	Thesis
Language:	English
Published:	2015
Subjects:	Statistical methods Multivariate analysis Geographic information systems
Online Access:	http://psasir.upm.edu.my/id/eprint/65490/1/FK%202015%20173IR.pdf
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spelling	my-upm-ir.654902018-09-20T04:15:54Z Gold potential mapping using bivariate and multivariate statistical models in GIS at Gua Musang, Malaysia 2015-01 Yusoff, Suhaimizi Throughout the history of humanity, gold remains as the most desired metals. Due to its small occurrences in the earth’s crust, this precious metal acquisition is very difficult. In Malaysia, gold potential map have been generated using conventional methods and are inadequate and lacking the ability to assess the accuracy. Develop new techniques has overcome this weakness and currently used around the world. The first time to be applied in Malaysia, this study aim to assess the capability of data-driven Geographical Information System (GIS) modelling technique for mapping gold potential areas of Kelantan, Malaysia. The study area is located at the south of Kelantan state, Malaysia borders Pahang state. The study area covers about 593 km2 and is situated approximately 186 km from Kota Bharu, Kelantan. In this study, six gold deposit controlling factors that influence the gold deposit occurrences were extracted from available maps and spatial databases. These controlling factors are:lithology, fault, geochemical data of Copper (Cu), Lead (Pb) and Tungsten (W) and geophysical data of Potassium (K). In the GIS environment, all six controlling factors were integrated and modelled based on data-driven technique. The generation of the gold potential map was carried out using two different types of GIS modelling techniques. The models applied are evidential belief functions (EBF) and logistic regression (LR). The spatial relationship between gold deposit and its controlling parameters was assessed. The predicted gold potential map was classified into four distinct zones based on the classification scheme from the literatures. The analysis and comparison of these results indicate that: (1) The gold potential map generated by EBF model is considered as the best results with prediction accuracy of 81%, (2) The gold potential map generated using LR model has low prediction accuracy of 62.67% and (3) The most influential controlling factors for gold deposit occurrences is lithology, followed by Cu, W, fault, K and Pb. The predicted gold potential map of the study area generated using EBF technique indicated that about 4.8% or 28.49 km2 are in the very high potential zone, with about 10.9% or 65.22 km2 in high potential zone, with about 38.7% or 229.6 km2 fall in the moderate potential zone, and about 45.6% or 269.69 km2 constituting the low potential zone. The results indicate that it can be used for future planning of gold exploration by providing a rapid reproduction approach with reduce time and cost. The results also demonstrate that this modelling technique may also apply to other area with similar parameters. Statistical methods Multivariate analysis Geographic information systems 2015-01 Thesis http://psasir.upm.edu.my/id/eprint/65490/ http://psasir.upm.edu.my/id/eprint/65490/1/FK%202015%20173IR.pdf text en public masters Universiti Putra Malaysia Statistical methods Multivariate analysis Geographic information systems
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
topic	Statistical methods Multivariate analysis Geographic information systems
spellingShingle	Statistical methods Multivariate analysis Geographic information systems Yusoff, Suhaimizi Gold potential mapping using bivariate and multivariate statistical models in GIS at Gua Musang, Malaysia
description	Throughout the history of humanity, gold remains as the most desired metals. Due to its small occurrences in the earth’s crust, this precious metal acquisition is very difficult. In Malaysia, gold potential map have been generated using conventional methods and are inadequate and lacking the ability to assess the accuracy. Develop new techniques has overcome this weakness and currently used around the world. The first time to be applied in Malaysia, this study aim to assess the capability of data-driven Geographical Information System (GIS) modelling technique for mapping gold potential areas of Kelantan, Malaysia. The study area is located at the south of Kelantan state, Malaysia borders Pahang state. The study area covers about 593 km2 and is situated approximately 186 km from Kota Bharu, Kelantan. In this study, six gold deposit controlling factors that influence the gold deposit occurrences were extracted from available maps and spatial databases. These controlling factors are:lithology, fault, geochemical data of Copper (Cu), Lead (Pb) and Tungsten (W) and geophysical data of Potassium (K). In the GIS environment, all six controlling factors were integrated and modelled based on data-driven technique. The generation of the gold potential map was carried out using two different types of GIS modelling techniques. The models applied are evidential belief functions (EBF) and logistic regression (LR). The spatial relationship between gold deposit and its controlling parameters was assessed. The predicted gold potential map was classified into four distinct zones based on the classification scheme from the literatures. The analysis and comparison of these results indicate that: (1) The gold potential map generated by EBF model is considered as the best results with prediction accuracy of 81%, (2) The gold potential map generated using LR model has low prediction accuracy of 62.67% and (3) The most influential controlling factors for gold deposit occurrences is lithology, followed by Cu, W, fault, K and Pb. The predicted gold potential map of the study area generated using EBF technique indicated that about 4.8% or 28.49 km2 are in the very high potential zone, with about 10.9% or 65.22 km2 in high potential zone, with about 38.7% or 229.6 km2 fall in the moderate potential zone, and about 45.6% or 269.69 km2 constituting the low potential zone. The results indicate that it can be used for future planning of gold exploration by providing a rapid reproduction approach with reduce time and cost. The results also demonstrate that this modelling technique may also apply to other area with similar parameters.
format	Thesis
qualification_level	Master's degree
author	Yusoff, Suhaimizi
author_facet	Yusoff, Suhaimizi
author_sort	Yusoff, Suhaimizi
title	Gold potential mapping using bivariate and multivariate statistical models in GIS at Gua Musang, Malaysia
title_short	Gold potential mapping using bivariate and multivariate statistical models in GIS at Gua Musang, Malaysia
title_full	Gold potential mapping using bivariate and multivariate statistical models in GIS at Gua Musang, Malaysia
title_fullStr	Gold potential mapping using bivariate and multivariate statistical models in GIS at Gua Musang, Malaysia
title_full_unstemmed	Gold potential mapping using bivariate and multivariate statistical models in GIS at Gua Musang, Malaysia
title_sort	gold potential mapping using bivariate and multivariate statistical models in gis at gua musang, malaysia
granting_institution	Universiti Putra Malaysia
publishDate	2015
url	http://psasir.upm.edu.my/id/eprint/65490/1/FK%202015%20173IR.pdf
_version_	1747812342735831040

Gold potential mapping using bivariate and multivariate statistical models in GIS at Gua Musang, Malaysia

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