Make sure to attend our paper presentations. You'll discover a new methodology that provides unsurpassed performances in kriging and simulations for mineral resource estimation and risk analysis. You also learn how geostatistics can help reduce nuclear facilities decommissioning costs by optimizing sampling design and groundwater monitoring networks.
Application of SPDE method to a continental-fluvial uranium ore body and comparison with classical methods
Big data management is currently a concern for most businesses and the mining industry is no exception. To boost productivity, a risk analysis associated with mineral resource estimation is often required and an assessment of the uncertainty is compulsory. Conditional simulations bring an appropriate answer but the traditional methodologies (e. g. Turning Bands Model) may lead to prohibitive computing time and the need to choose a moving neighborhood, which is a conundrum. Most of the Geostatistical standard procedures suffer from severe limitations in the presence of large data sets and numerous target sites. The Stochastic Partial Differential Equation (SPDE) approach offers an innovative way of calculating Kriging and Geostatistical Simulations and leads to a very significant gain in the performance. […]
by Marie-Cécile Febvey, Geovariances, Nicolas Desassis, MINES ParisTech, Olivier Masset, Orano Group
Keywords: Simulations, Stochastic Partial Differential Equations, Unique Neighborhood
Geostatistical deconvolution for 2D radiological characterizations
In the nuclear industry, facility dismantling and decommissioning projects as well as site remediation projects are current challenging issues. Precise appraisal of contamination state is a prerequisite. Radiological evaluations have multiple objectives to be considered: determination of average activity levels, to allow the categorization of surfaces or volumes (sorted into different radioactive waste categories); identification of hot spots, i.e. small areas with high activity levels; and estimation of total activity (source term as an accumulation). The problem of deconvolution arises from a recurring problem around the input data: the values are not strictly punctual but associated with a measurement or sampling support. […]
by Yvon Desnoyers and Pedram Masoudi, Geovariances
Spatio-temporal optimization of groundwater monitoring network at Pickering Nuclear Generating Station
CANada Deuterium Uranium (CANDU) reactors have been in operation since the 1950s. Nowadays CANDU Owners Group (COG) is actively pursuing Strategic Research and Development to improve CANDU decommissioning and remediation activities. As part of this initiative, sampling optimization and characterization methods for soil and water are being pursued as these can significantly reduce decommissioning costs. The adequacy of current approaches in distributed screening networks is questioned by calling statistical and geostatistical criteria. Opportunities for improvement have been identified to support the optimization of the number and location of sampling points at CANDU nuclear sites. […]
by Yvon Desnoyers and Pedram Masoudi, Geovariances, Mike Grey, Kinectrics.
Keywords: Tritium monitoring, undergoing active decommissioning, time-space characterization, sampling optimization, groundwater surveying, temporal correlation
Sampling optimization for radiological characterization: a spatial inventory
Dismantling and decommissioning of nuclear facilities or remediation of contaminated sites are industrial projects with huge challenges. The contamination characterization phase should be efficient, and the sampling strategy has to be rational. However, investigations also represent capital expenditure; the cost of radiation protection constraints and laboratory analysis can represent a large amount of money, depending on the radionuclide. Therefore the entire sampling strategy should be optimized to reduce useless samples and unnecessary measures. Within the geostatistics framework, the spatial structure of radioactive contamination makes the optimization of sampling (number and position of data points) particularly relevant. […]
by Yvon Desnoyers, Geovariances