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Despite the importance of peatlands as carbon reservoirs, a reliable methodology for the detection of peat volumes at regional scale is still missing. In this study we explore for the first time the use of airborne electromagnetic (AEM) to detect and quantify peat thickness and extension of two bogs located in Norway, where peat lays over resistive bedrock. Our results show that when calibrated using a small amount of field measurements, AEM can successfully detect peat volume even in less ideal conditions, that is, relatively resistive peat over resistive substrata. We expect the performance of AEM to increase significantly in presence of a conductive substratum without need of calibration with field data. The organic carbon content retrieved from field surveys and laboratory analyses combined with the 3‐D model of the peat extracted from AEM allowed us to quantify the total organic carbon of the selected bogs, hence assessing the carbon pool.

Excavation and piling works related to seafront development in Oslo's historic harbour area need to mitigate the risk of damaging buried archaeological objects. In the Bjørvika harbour in Oslo, Norway, electrical resistivity tomography was performed to detect structures with potential archaeological value. A 2.5 dataset consisting of four equally spaced parallel lines was collected, trimmed, and systematically processed with both 2D and 3D inversion routines. The results were in good agreement with known underground features, and for the present dataset, an iteratively reweighted least squares 2D inversion was clearly preferable over a 3D inversion. This conclusion is based on differences in model resolution, data processing costs, and the value of the final product for engineering decision-making.agreeing well with rectangular wooden quay or wharf structures later excavated (Bazin et al. 2012;Pfaffhuber et al. 2012).The present 2.5D dataset allows for both 2D and 3D inversions, and was acquired by the Norwegian Geotechnical Institute on behalf of HAV Eiendom on a harbour property intended for an energy central unit and a beach-service building.The inversion algorithm and parameters should be carefully chosen according to the data and problem at hand. A popular approach is the standard smoothness-constrained least squares (SCLS) inversion (also known as the Occam inversion presented by Constable et al. (1987)), which creates a ground model fitting the data with minimum complexity. However, in case the data are especially noisy and/or the real resistivity distribution features large contrasts and blocky or layered structures, the smoothness-constrained inversion tends to smear out real boundaries and adjusts the model for outliers and erroneous data. In such cases, the iteratively reweighted least squares (IRLS) inversion (Farquharson and Oldenburg 1998;Loke, Acworth and Dahlin 2003) has proven to be more appropriate.We aim to analyse the engineering usability of two different implementations of the IRLS inversion algorithm by comparing how well the results fit with the ground truth and to determine whether 3D processing gives enough additional information on satisfying quality for the additional cost and effort.

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