The Listafjord–Drangedal Fault Complex is a central structure in the NE-SW-trending Agder–Telemark Lineament Zone that dominates the structural grain and topography of southernmost tip of Norway. The fault can be followed for a distance of more than 170 km from the shelf area off Listafjorden–Fedafjorden in Vest Agder county to Drangedal in Telemark county. It has been analyzed by the use of digital topographic, remote sensing and potential field data, supported by field investigations. At least seven separate left-stepping fault segments have been identified. These are characterized by numerous internal fault lenses, separate fault strands and fault splays, partly displaying contrasting fault attitude and style of deformation. The northeastern termination of the Listafjord–Drangedal Fault Complex consists of fanning fault branches (horse-tailing), whereas its southwestern termination is buried below sediments in the continental shelf and remains obscure. The fault rocks of the various fault segments include cataclasites and mylonites that in places are interlayered with zones of fault gouge. By tentative correlation to the Hunnedalen dyke system in Rogaland, the age of initiation for the Listafjord–Drangedal Fault Complex is suggested to be Late Proterozoic. Parts of the fault complex were affected by at least two stages of faulting including (dextral?) shear and top-to-the-SE extension. The latter stage is assumed to be of post-Caledonian age, and recent seismic activity suggests that this ancient structural grain is still seismically active.
The flight altitude has a large effect on the airborne electromagnetic (AEM) responses. Due to the dynamic environment of the aircraft, the recorded sensor altitudes may contain errors. Research demonstrates that the AEM responses caused by a several meters altitude errors can be larger than caused by some anomalous body. Ignoring these errors will create erroneous results in AEM data interpretation. Considering that there is not yet a published 3D AEM inversion method that takes into account the flight altitude, we develop in this paper a 3D inversion algorithm for AEM with the flight height treated as an inversion parameter. For the forward modeling we use the finite element method, while for the inversion we use the Gauss-Newton optimization method. To make our inversion works for variable flight altitudes, we propose a scheme of 3D Jacobean matrix calculation for both the resistivities and flight altitudes without much increasing the computational cost. The numerical simulation result confirms that the flight altitude really has a large effect on the AEM responses. The inversions of synthetic data show that our 3D inversion method can both recover the resistivity distribution in the underground and decrease the altitude errors recorded, while the field data inversion demonstrates that our method can deliver a better inversion model with a smaller data misfit.
From the first use of airborne electromagnetic (AEM) systems for remote sensing in the 1950s, AEM data acquisition, processing and inversion technology have rapidly developed. Once used extensively for mineral exploration in its early days, the technology is increasingly being applied in other industries alongside ground-based investigation techniques. This paper reviews the application of onshore AEM in Norway over the past decades. Norway’s rugged terrain and complex post-glacial sedimentary geology have contributed to the later adoption of AEM for widespread mapping compared to neighbouring Nordic countries. We illustrate AEM’s utility by using two detailed case studies, including time-domain and frequency domain AEM. In both cases, we combine AEM with other geophysical, geological and geotechnical drillings to enhance interpretation, including machine learning methods. The end results included bedrock surfaces predicted with an accuracy of 25% of depth, identification of hazardous quick clay deposits, and sedimentary basin mapping. These case studies illustrate that although today’s AEM systems do not have the resolution required for late-phase, detailed engineering design, AEM is a valuable tool for early-phase site investigations. Intrusive, ground-based methods are slower and more expensive, but when they are used to complement the weaknesses of AEM data, site investigations can become more efficient. With new developments of drone-borne (UAV) systems and increasing investment in AEM surveys, we see the potential for continued global adoption of this technology.
It is estimated that exposure to radon in Norwegian dwellings is responsible for as many as 300 deaths a year due to lung cancer. To address this, the authorities in Norway have developed a national action plan that has the aim of reducing exposure to radon in Norway (Norwegian Ministries, 2010). The plan includes further investigation of the relationship between radon hazard and geological conditions, and development of map-based tools for assessing the large spatial variation in radon hazard levels across Norway. The main focus of the present contribution is to describe how we generate map predictions of radon potential (RP), a measure of radon hazard, from available airborne gamma ray spectrometry (AGRS) surveys in Norway, and what impact these map predictions can be expected to have on radon protection work including land-use planning and targeted surveying. We have compiled 11 contiguous AGRS surveys centred on the most populated part of Norway around Oslo to produce an equivalent uranium map measuring 180 km × 102 km that represents the relative concentrations of radon in the near surface of the ground with a spatial resolution in the 100 s of metres. We find that this map of radon in the ground offers a far more detailed and reliable picture of the distribution of radon in the sub-surface than can be deduced from the available digital geology maps. We tested the performances of digital geology and AGRS data as predictors of RP. We find that digital geology explains approximately 40% of the observed variance in ln RP nationally, while the AGRS data in the Oslo area split into 14 bands explains approximately 70% of the variance in the same parameter. We also notice that there are too few indoor data to characterise all geological settings in Norway which leaves areas in the geology-based RP map in the Oslo area, and elsewhere, unclassified. The AGRS RP map is derived from fewer classes, all characterised by more than 30 indoor measurements, and the corresponding RP map of the Oslo area has no unclassified parts. We used statistics of proportions to add 95% confidence limits to estimates of RP on our predictive maps, offering public health strategists an objective measure of uncertainty in the model. The geological and AGRS RP maps were further compared in terms of their performances in correctly classifying local areas known to be radon affected and less affected. Both maps were accurate in their predictions; however the AGRS map out-performed the geology map in its ability to offer confident predictions of RP for all of the local areas tested. We compared the AGRS RP map with the 2015 distribution of population in the Oslo area to determine the likely impact of radon contamination on the population. 11.4% of the population currently reside in the area classified as radon affected. 34% of ground floor living spaces in this affected area are expected to exceed the maximum limit of 200 Bq/m3, while 8.4% of similar spaces outside the affected area exceed this same limit, indicating that the map is very efficient at separating areas with quite different radon contamination profiles. The usefulness of the AGRS RP map in guiding new indoor radon surveys in the Oslo area was also examined. It is shown that indoor measuring programmes targeted on elevated RP areas could be as much as 6 times more efficient at identifying ground floor living spaces above the radon action level compared with surveys based on a random sampling strategy. Also, targeted measuring using the AGRS RP map as a guide makes it practical to search for the worst affected homes in the Oslo area: 10% of the incidences of very high radon contamination in ground floor living spaces (≥800 Bq/m3) are concentrated in just 1.2% of the populated part of the area.
Summary Large-scale airborne geophysical data are an important part of regional-scale mineral exploration and in that context NGU conducted an airborne magnetic and radiometric survey over the Kviteseid, Notodden and Ulefoss regions, in Telemark county in summer 2013 as a part of the MINS project (Mineral resources in South Norway). Results from a previous high resolution helicopter-borne gamma-ray spectrometry survey that was carried out in 2006 over the Fen Complex, Ulefoss region, were reevaluated in the light of the new data from the regional survey of the MINS project. We compare the thorium (eTh) ground concentrations of the two surveys, and although they have similar pattern, are showing significant scaling difference. The differences in the level of ground concentrations between two spectrometry surveys flown over the same region were investigated. Reprocessing of the old data revealed that the discrepancies arose from inappropriate selection of sensitivity coefficients during the calculation of elemental ground concentrations from height corrected gamma-ray counts. Therefore, correct calibration of the spectrometers are essential for the quality of the processed data.
We compute the first probabilistic uranium concentration map of Norway. Such a map can support mineral exploration, geochemical mapping, or the assessment of the health risk to the human population. We employ multiple non-linear regression to fill the information gaps in sparse airborne and ground-borne uranium data sets. We mimic an expert elicitation by employing Random Forests and Multi-layer Perceptrons as digital agents equally qualified to find regression models. In addition to the regression, we use supervised classification to produce conservative and alarmistic classified maps outlining regions with different potential for the local occurrence of uranium concentration extremes. Embedding the introduced digital expert elicitation in a Monte Carlo approach we compute an ensemble of plausible uranium concentrations maps of Norway discretely quantifying the uncertainty resulting from the choice of the regression algorithm and the chosen parametrization of the used regression algorithms. We introduce digitated glyphs to visually integrate all computed maps and their associated uncertainties in a loss-free manner to fully communicate our probabilistic results to map perceivers. A strong correlation between mapped geology and uranium concentration is found, which could be used to optimize future sparse uranium concentration sampling to lower extrapolation components in future map updates.
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