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...

وصف كامل

التفاصيل البيبلوغرافية
المؤلف الرئيسي: Bala, Muhammad Sabiu
التنسيق: أطروحة
اللغة:الإنجليزية
منشور في: 2018
الموضوعات:
الوصول للمادة أونلاين:http://eprints.usm.my/44169/
Abstract Abstract here
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author Bala, Muhammad Sabiu
author_facet Bala, Muhammad Sabiu
author_sort Bala, Muhammad Sabiu
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.
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record_pdf Abstract
spelling usm-441692019-04-23T01:16:38Z http://eprints.usm.my/44169/ Multiple Linear Regression Models For Estimating True Subsurface Resistivity From Apparent Resistivity Measurements 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 NonPeerReviewed application/pdf en http://eprints.usm.my/44169/1/MUHAMMAD%20SABIU%20BALA.pdf Bala, Muhammad Sabiu (2018) Multiple Linear Regression Models For Estimating True Subsurface Resistivity From Apparent Resistivity Measurements. PhD thesis, Universiti Sains Malaysia.
spellingShingle QC1 Physics (General)
Bala, Muhammad Sabiu
Multiple Linear Regression Models For Estimating True Subsurface Resistivity From Apparent Resistivity Measurements
title 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_short 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
topic QC1 Physics (General)
url http://eprints.usm.my/44169/
work_keys_str_mv AT balamuhammadsabiu multiplelinearregressionmodelsforestimatingtruesubsurfaceresistivityfromapparentresistivitymeasurements