IP interpretation in environmental investigations

Abstract

The induced polarization (IP) response of rocks and soils is a function of lithology and fluid conductivity. IP measurements are sensitive to the low‐frequency capacitive properties of rocks and soils, which are controlled by diffusion polarization mechanisms operating at the grain‐fluid interface. IP interpretation typically is in terms of the conventional field IP parameters: chargeability, percentage frequency effect, and phase angle. These parameters are dependent upon both surface polarization mechanisms and bulk (volumetric) conduction mechanisms. Consequently, they afford a poor quantification of surface polarization processes of interest to the field geophysicist. A parameter that quantifies the magnitude of surface polarization is the normalized chargeability, defined as the chargeability divided by the resistivity magnitude. This parameter is proportional to the quadrature conductivity measured in the complex resistivity method. For nonmetallic minerals, the quadrature conductivity and normalized chargeability are closely related to lithology (through the specific surface area) and surface chemistry. Laboratory and field experiments were performed to determine the dependence of the standard IP parameters and the normalized chargeability on two important environmental parameters: salinity and clay content. The laboratory experiments illustrate that the chargeability is strongly correlated with the sample resistivity, which depends on salinity, porosity, saturation, and clay content. The normalized chargeability is shown to be independent of the sample resistivity and it is proportional to the quadrature conductivity, which is directly related to the surface polarization processes. Laboratory‐derived relationships between conductivity and salinity, and normalized chargeability and clay content, are extended to the interpretation of 1‐D and 2‐D field‐IP surveys. In the 2‐D survey, the apparent conductivity and normalized chargeability data are used to segment the images into relatively clay‐free and clay‐rich zones. A similar approach can eventually be used to predict relative variations in the subsurface clay content, salinity and, perhaps, contaminant concentrations.