Estimation Of Near Surface Soils’ Porosity Using Resistivity Imaging Data - Rosli, Najmiah (2020)
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,