The Hemerdon W–Sn deposit, being worked at Drakelands Mine near Plymouth by Wolf Minerals, is centered upon a subvertical, NNE-SSW striking, 100+ m wide Early Permian granite dyke hosted by Devonia...

Radon exposure is considered to cause several hundred fatalities from lung-cancer each year in Norway. A national map identifying areas which are likely to be exposed to elevated radon concentrations would be a useful tool for decision-making authorities, and would be particularly important in areas where only few indoor radon measurements exist. An earlier Norwegian study (Smethurst et al. 2008) produced radon hazard maps by examining the relationship between airborne gamma-ray spectrometry, bedrock and drift geology, and indoor radon. The study was limited to the Oslo region where substantial indoor radon and airborne equivalent uranium datasets were available, and did not attempt to test the statistical significance of relationships, or to quantify the confidence of its predictions. While it can be anticipated that airborne measurements may have useful predictive power for indoor radon, airborne measurement coverage in Norway is at present sparse; to provide national coverage of radon hazard estimates, a good understanding of the relationship between geology and indoor radon is therefore important. In this work we use a new enlarged (n = 34,563) form of the indoor radon dataset with national coverage, and we use it to examine the relationship between geology and indoor radon concentrations. We use this relationship to characterise geological classes by their radon potential, and we produce a national radon hazard map which includes confidence limits on the likelihood of areas having elevated radon concentrations, and which covers the whole of mainland Norway, even areas where little or no indoor radon data are available. We find that bedrock and drift geology classes can account for around 40% of the total observed variation in radon potential. We test geology-based predictions of RP (radon potential) against locally-derived estimates of RP, and produce classification matrices with kappa values in the range 0.37-0.56. Our classifier has high predictive value but suffers from low sensitivities for radon affected areas. We investigate an alternative classification method based on a Naive Bayes classifier which results in similar overall performance. The work forms part of an ongoing study which will eventually incorporate airborne equivalent uranium data, as and when new airborne data become available. (C) 2016 Elsevier Ltd. All rights reserved.

The South West England Orefield is well-known for its polymetallic magmatichydrothermal mineralisation associated with the composite Cornubian granite batholith. The mineralisation contains elevated concentrations of a range of metals (e.g. W, Sb, Bi, As, Be, Cd, Ga, Ge, In, Li, Nb, Ta) used in high-technology and clean energy applications and for which security of supply concerns exist [1,2,3]. Exploration for these elements requires improved understanding of the spatial distribution of granite types and their relationship to the different mineralisation styles and parageneses. Previous granite classifications defined granite types based on mineralogical and/or textural observations from field sampling [e.g. 4]. However, these divisions over simplify mineralogical variation (e.g. micas), and the sample density is inadequate to reflect the heterogeneity of the batholith. Classifications based on whole-rock geochemistry, provide a more objective classification method [e.g. 5] and can guide mineral exploration, particularly in poorly exposed areas.

This paper presents the 4DPlates, an application designed to display high resolution data and reconstruct their positions in the geologic past. 4DPlates makes use of level of detail (LoD) grids with a 4–8 tree structure to store the data so only the required resolution for a particular viewpoint is used. This facility means that the user can interact with large data sets on the fly, achieving between 30 and 50 frames per second with a large test data set. The article presents the design and functionality of the application from view-dependent visualization to the ability to reconstruct data in the distant past. Finally, we apply the application in two geoscientific settings. In the first, we calculate the tectonic subsidence from sediment loading and test the variation of the sediment density to the resulting subsidence grid. Secondly, we examine two South Atlantic reconstructions and highlight minor differences between them visible in the closeness of the fit of the topographic grids. The application excels at providing an interactive manipulation of high resolution data, whether it be reconstructing the data, setting the lighting angle or vertical exaggeration, or modifying parameters in the underlying formulas.

In recent years carborne gamma spectrometry has expanded from its role as a geological survey platform to serving as a useful asset in the field of emergency response to radiological and nuclear situations. Its two main applications are searching for orphan sources and for surveying in the aftermath of an accident involving the release of radioactive materials. Despite this expansion, the oppor-tunities for gaining practical experience in the field are limited by cost considera-tions and practicability. These limitations are exacerbated by the fact that data generated and displayed in the field differ significantly from gamma spectral data generated in a laboratory environment. As a means of exercising existing emer-gency measuring/surveying capability and introducing carborne measurements to a larger group, a virtual exercise was devised. The exercise ORPEX (Orphan Sources and Fresh Fallout Virtual Exercise in Mobile Measurement) featured two typical emergency scenarios in which carborne measuring systems might be de-ployed: firstly a search for multiple orphan sources and secondly surveying to delineate patchy fallout from a local release point. In the first scenario, synthetic spectral data were generated for imaginary point sources and inserted into genu-ine carborne measurements from in the Trondheim area of Norway. Participants were presented with a typical software tool and data in a range of typical formats and asked to report the source locations and isotopes within a time limit. In the second scenario, synthetic spectral data representing fallout from a local fire in-volving radioactive material were added to real carborne data from the Trondheim area. Participants were asked to produce maps that identify and characterise the regions of contamination within the same time limit. Fourteen individual organi-sations from seven different countries supplied results. Results from participants indicate that for strong sources of isotopes with simple spectra featuring high energy peaks, location and identification is not a problem. Problems arise for isotopes with low energy signals or that present a weak signal even when visible for extended periods. Experienced analysts tended to perform better in identifica-tion of sources even if they were inexperienced in mobile measurements whereas those with experience in such measurements were more confident in providing more precise estimates of location. The results indicated the need for the

During the RESUME 99 exercise, the radiocaesium (Cs) activity in the surroundings of Gavle in central Sweden was mapped using car-borne gamma-ray spectrometry (CGS). The CGS data along with airborne gamma-ray spectrometry (AGS) data from the same area have been used to examine possible correlations between the CGS and AGS results, detector type and position, and geographical information, such as land-use and road type. The overall differences between various CGS results are small, while larger differences are found between AGS and CGS results. In general only little correlation was found with land-use and with road-type and width. The differences between AGS and CGS results arise because airborne detectors have a different field of view than a ground-based detector. From an analysis of the depth-dependency of AGS and CGS data for a depth-distributed source, it is found that the mean mass depth may be inferred from the ratio of AGS to CGS spectral count rates. Integration of AGS and CGS data requires a precise definition of quantities and units for reporting activity concentrations in a complicated geometry and care must be taken to translate AGS results into equivalent CGS quantities taking into account the spatial distribution of the radionuclides.