Induction effect removal for High Frequency Induced Polarization data

Summary In the analysis of Induced Polarization (IP) data, it is commonly assumed that induction effects (IE) are negligible. However, at higher frequencies, this assumption becomes increasingly invalid, posing challenges for IP measurements. High-frequency induced polarization (HFIP) extends the conventional IP frequency range beyond 100 kHz, allowing estimation of ice content by capturing the characteristic decrease in permittivity of water ice. This study focuses on the interpretation of HFIP data while accounting for IE. We modified an existing one-dimensional simulation code to evaluate HFIP responses over frozen ground with ice, both with and without the influence of IE. Our results demonstrate that IE can distort HFIP measurements in typical permafrost conditions, potentially obscuring the characteristic polarization behavior of water ice. two-dimensional IP inversion codes that account for IE are not routinely available. Even if they were, the simultaneous presence of IE and IP would likely complicate their application. We therefore propose to remove IE from the data, with the aim to use well-established two-dimensional SIP inversion routines. For this purpose, we implemented a one-dimensional inversion routine that includes IE as a frequency-dependent correction factor. The method is based on fitting a layered model to the data. Comparing the responses with and without IE, we calculate a correction factor which is subsequently used to remove IE from the original dataset. The method is conservative in the sense that features not well matched by the one-dimensional inversion are preserved and no information gets lost. We demonstrate the effectiveness of the method with synthetic data, as well as field datasets from an unfrozen site in Germany, and from permafrost peatlands in Scandinavia. We provide evidence from reciprocal measurements that cable coupling effects, not included in the correction procedure, have been effectively minimized by the acquisition system. We further show that high-frequency phase shifts are strongly influenced by IE, and that the correction methodology successfully restores the spectral response. By applying the approach to a measured dataset, we demonstrate that two-dimensional inversion of the corrected data with a well-established code is both feasible and robust. The resulting model deviates markedly from that obtained with uncorrected data, highlighting the critical role of the correction procedure for reliable interpretation.