Three-dimensional (3D) models are often utilised to assess the presence of sand and gravel deposits. Expanding these models to provide a better indication of the suitability of the deposit as aggregate for use in construction would be advantageous. This, however, leads to statistical challenges. To be effective, models must be able to reflect the interdependencies between different criteria (e.g. depth to deposit, thickness of deposit, ratio of mineral to waste, proportion of ‘fines’) as well as the inherent uncertainty introduced because models are derived from a limited set of boreholes in a study region. Using legacy borehole data collected during a systematic survey of sand and gravel deposits in the UK, we have developed a 3D model for a 2400 km2 region close to Reading, southern England. In developing the model, we have reassessed the borehole grading data to reflect modern extraction criteria and explored the most suitable statistical modelling technique. The additive log-ratio transform and the linear model of coregionalization have been applied, techniques that have been previously used to map soil texture classes in two dimensions, to assess the quality of sand and gravel deposits in the area. The application of these statistical techniques leads to a model which can be used to generate thousands of plausible realisations of the deposit which fully reflect the extent of model uncertainty. The approach offers potential to improve regional-scale mineral planning by providing an enhanced understanding of sand and gravel deposits and the extent to which they meet current extraction criteria.
Russian Journal of Ecology - A simultaneous analysis of the chronographic variation of the mandible of bank vole (Clethrionomys glareolus Shreb.) in three longitudinally distant populations that... 相似文献
Russian Journal of Ecology - It has been shown that the main drivers of the dynamics of cladoceran and copepod abundances can be predators (fish), the quantity and/or quality of food in terms of... 相似文献
Building a community that is resilient to disasters has become one of the main goals of disaster management. Communities that are more disaster resilient often experience less impact from the disaster and reduced recovery periods afterwards. This study develops a methodology for constructing a set of indicators measuring Community Disaster Resilience Index (CDRI) in terms of human, social, economic, environmental, and institutional factors. In this study, the degree of community resilience to natural disasters was measured for 229 local municipalities in Korea, followed by an examination of the relationship between the aggregated CDRI and disaster losses, using an ordinary least squares (OLS) regression method and a geographically weighted regression (GWR) method. Identifying the extent of community resilience to natural disasters would provide emergency managers and decision-makers with strategic directions for improving local communities' resilience to natural disasters while reducing the negative impacts of disasters. 相似文献