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: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2018
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| Subjects: | |
| Online Access: | http://eprints.usm.my/44169/1/MUHAMMAD%20SABIU%20BALA.pdf |
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| Summary: | 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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