Multiple Linear Regression Models For Estimating True Subsurface Resistivity From Apparent Resistivity Measurements

Multiple linear regression (MLR) models for rapid estimation of true subsurface resistivity from apparent resistivity measurements are developed and assessed in this study. The objective is to minimize the processing time required to carry out inversion with conventional algorithms. The arrays consi...

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Main Author:	Bala, Muhammad Sabiu
Format:	Thesis
Language:	English
Published:	2018
Subjects:	QC1 Physics (General)
Online Access:	http://eprints.usm.my/44169/1/MUHAMMAD%20SABIU%20BALA.pdf
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id	my-usm-ep.44169
record_format	uketd_dc
spelling	my-usm-ep.441692019-04-23T01:16:38Z Multiple Linear Regression Models For Estimating True Subsurface Resistivity From Apparent Resistivity Measurements 2018-06 Bala, Muhammad Sabiu QC1 Physics (General) Multiple linear regression (MLR) models for rapid estimation of true subsurface resistivity from apparent resistivity measurements are developed and assessed in this study. The objective is to minimize the processing time required to carry out inversion with conventional algorithms. The arrays considered are Wenner, Wenner-Schlumberger and Dipole-dipole. The parameters investigated are apparent resistivity ( a  ), horizontal location (x) and depth (z) as independent variable; while true resistivity ( t  ) is dependent variable. 2018-06 Thesis http://eprints.usm.my/44169/ http://eprints.usm.my/44169/1/MUHAMMAD%20SABIU%20BALA.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Pengajian Sains Fizik
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	QC1 Physics (General)
spellingShingle	QC1 Physics (General) Bala, Muhammad Sabiu Multiple Linear Regression Models For Estimating True Subsurface Resistivity From Apparent Resistivity Measurements
description	Multiple linear regression (MLR) models for rapid estimation of true subsurface resistivity from apparent resistivity measurements are developed and assessed in this study. The objective is to minimize the processing time required to carry out inversion with conventional algorithms. The arrays considered are Wenner, Wenner-Schlumberger and Dipole-dipole. The parameters investigated are apparent resistivity ( a  ), horizontal location (x) and depth (z) as independent variable; while true resistivity ( t  ) is dependent variable.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Bala, Muhammad Sabiu
author_facet	Bala, Muhammad Sabiu
author_sort	Bala, Muhammad Sabiu
title	Multiple Linear Regression Models For Estimating True Subsurface Resistivity From Apparent Resistivity Measurements
title_short	Multiple Linear Regression Models For Estimating True Subsurface Resistivity From Apparent Resistivity Measurements
title_full	Multiple Linear Regression Models For Estimating True Subsurface Resistivity From Apparent Resistivity Measurements
title_fullStr	Multiple Linear Regression Models For Estimating True Subsurface Resistivity From Apparent Resistivity Measurements
title_full_unstemmed	Multiple Linear Regression Models For Estimating True Subsurface Resistivity From Apparent Resistivity Measurements
title_sort	multiple linear regression models for estimating true subsurface resistivity from apparent resistivity measurements
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Sains Fizik
publishDate	2018
url	http://eprints.usm.my/44169/1/MUHAMMAD%20SABIU%20BALA.pdf
_version_	1747821337026494464

Multiple Linear Regression Models For Estimating True Subsurface Resistivity From Apparent Resistivity Measurements

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