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Estimation Of Near Surface Soils’ Porosity Using Resistivity Imaging Data

Two-dimensional resistivity imaging (2-DRI) is a widely employed method in ground studies, which includes porosity estimations due to its high sensitivity to slight electrical resistivity variations. Porosity has significant influence on other ground properties and is conventionally is obtained t...

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Main Author: Rosli, Najmiah
Format: Thesis
Language:English
Published: 2020
Subjects:
QC1 Physics (General)
Online Access:http://eprints.usm.my/55116/1/NAJMIAH%20BINTI%20ROSLI%20-%20TESIS%20cut.pdf
http://eprints.usm.my/55116/
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spelling my.usm.eprints.55116 http://eprints.usm.my/55116/ Estimation Of Near Surface Soils’ Porosity Using Resistivity Imaging Data Rosli, Najmiah QC1 Physics (General) Two-dimensional resistivity imaging (2-DRI) is a widely employed method in ground studies, which includes porosity estimations due to its high sensitivity to slight electrical resistivity variations. Porosity has significant influence on other ground properties and is conventionally is obtained through physical samplings, which are costly and time consuming; thus, Archie’s equation is commonly employed to estimate a material’s porosity. However, most studies still conduct laboratory measurements on soil samples to obtain the values for Archie’s variables such as cementation exponent and pore-fluid resistivity before calculating porosity for the targeted area. This demonstrates that no method is yet available to accurately estimate porosity without physical samplings. This study comes up with a novel approach (SPyCRID) to effectively estimate porosity of soils using 2-DRI data that is sample-free. Focusing only on unconsolidated soils, this study demonstrates the development of SPyCRID, where its calibrations were conducted using two models to represent different fine grains’ percentages with fresh and brackish pore-fluid conditions. Archie’s variables; pore-fluid resistivity and bulk resistivity of saturated soil, were extracted from 2-DRI inversion model. With fixed cementation exponent value, all of Archie’s variables are now satisfied and became input in SPyCRID to estimate each model’s soil porosity prior to data iterations. Considering that SPyCRID generates >20 data sets in the iterations, data constraints were established to assist in selecting data sets with Archie’s values that best represents the soil. The data constraints are based on Waxman-Smits’ regression gradient, the number of data points used, 2020-07 Thesis NonPeerReviewed application/pdf en http://eprints.usm.my/55116/1/NAJMIAH%20BINTI%20ROSLI%20-%20TESIS%20cut.pdf Rosli, Najmiah (2020) Estimation Of Near Surface Soils’ Porosity Using Resistivity Imaging Data. PhD thesis, Universiti Sains Malaysia.
institution Universiti Sains Malaysia
building Hamzah Sendut Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Sains Malaysia
content_source USM Institutional Repository
url_provider http://eprints.usm.my/
language English
topic QC1 Physics (General)
spellingShingle QC1 Physics (General)
Rosli, Najmiah
Estimation Of Near Surface Soils’ Porosity Using Resistivity Imaging Data
description Two-dimensional resistivity imaging (2-DRI) is a widely employed method in ground studies, which includes porosity estimations due to its high sensitivity to slight electrical resistivity variations. Porosity has significant influence on other ground properties and is conventionally is obtained through physical samplings, which are costly and time consuming; thus, Archie’s equation is commonly employed to estimate a material’s porosity. However, most studies still conduct laboratory measurements on soil samples to obtain the values for Archie’s variables such as cementation exponent and pore-fluid resistivity before calculating porosity for the targeted area. This demonstrates that no method is yet available to accurately estimate porosity without physical samplings. This study comes up with a novel approach (SPyCRID) to effectively estimate porosity of soils using 2-DRI data that is sample-free. Focusing only on unconsolidated soils, this study demonstrates the development of SPyCRID, where its calibrations were conducted using two models to represent different fine grains’ percentages with fresh and brackish pore-fluid conditions. Archie’s variables; pore-fluid resistivity and bulk resistivity of saturated soil, were extracted from 2-DRI inversion model. With fixed cementation exponent value, all of Archie’s variables are now satisfied and became input in SPyCRID to estimate each model’s soil porosity prior to data iterations. Considering that SPyCRID generates >20 data sets in the iterations, data constraints were established to assist in selecting data sets with Archie’s values that best represents the soil. The data constraints are based on Waxman-Smits’ regression gradient, the number of data points used,
format Thesis
author Rosli, Najmiah
author_facet Rosli, Najmiah
author_sort Rosli, Najmiah
title Estimation Of Near Surface Soils’ Porosity Using Resistivity Imaging Data
title_short Estimation Of Near Surface Soils’ Porosity Using Resistivity Imaging Data
title_full Estimation Of Near Surface Soils’ Porosity Using Resistivity Imaging Data
title_fullStr Estimation Of Near Surface Soils’ Porosity Using Resistivity Imaging Data
title_full_unstemmed Estimation Of Near Surface Soils’ Porosity Using Resistivity Imaging Data
title_sort estimation of near surface soils’ porosity using resistivity imaging data
publishDate 2020
url http://eprints.usm.my/55116/1/NAJMIAH%20BINTI%20ROSLI%20-%20TESIS%20cut.pdf
http://eprints.usm.my/55116/
_version_ 1746211089024024576
score 13.211869

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