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: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2020
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| Subjects: | |
| Online Access: | http://eprints.usm.my/55116/1/NAJMIAH%20BINTI%20ROSLI%20-%20TESIS%20cut.pdf |
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| Summary: | 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, |
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