Resistivity vs Temperature Laboratory Experiments on Arctic Sediments: Quantifying the Effects of Texture, Salt, and Cryostructure

When investigating Arctic permafrost sediments, Electrical Resistivity Tomography (ERT) is becoming increasingly popular due to its robust, relatively quick and non-invasive application. The interpretation of ERT data is often constrained by the knowledge of the geophysical properties of the encountered frozen materials, thereby highlighting the need for ERT calibration experiments. Lab experiments on samples can quantify the dependency of electrical resistivity on sediment temperatures. Variation in electrical resistivity also depends on sediment composition, ground ice structures and their orientation with respect to the array, and porewater chemistry, all of which need to be considered in interpreting field measurements.

This study aims to improve our interpretation of ERT field measurements by investigating controlling and limiting factors of validating measurements by laboratory tests. We performed these laboratory tests on synthetic mixtures and field samples, varying sample size, electrode array orientation, electrode spacing, electrode type and anisotropy. Samples were thawed and then refrozen during the tests to include hysteresis effects. Synthetic samples were built to provide known anisotropies. Field samples were used from sites in Canada, and on Greenland and Svalbard. Relationships between apparent electrical resistivity and temperature were compared with hydro-chemical analyses of sediment porewater, grain size and ice content.

The tests on artificial samples helped improving our experiment design and highlighted the importance of anisotropy in comparison with the effects spacing or sample sizes. The field samples showed the importance of ice content and cryostructures as well as high salt content on the temperature-resistivity curves. Our research enables a better understanding of the temperature-resistivity dependency, provides information on sample sizes and anisotropy limitations necessary for fieldwork sampling, and overall allows for a better understanding and therefore interpretation of temperature dependent ERT datasets.