Bechler A.,French National Institute for Agricultural Research |
Bechler A.,CEA Saclay Nuclear Research Center |
Romary T.,MINES ParisTech |
Jeannee N.,Geovariances |
Stochastic Environmental Research and Risk Assessment | Year: 2013
Geostatistics applied to radiological evaluation of nuclear premises provides methods to estimate radiological activities, together with their associated uncertainty. It enables a sophisticated sampling methodology combining radiation map and destructive samples. The radiological assessment is divided in two steps: first, a regular control of the surface activity is performed. Then, to assess the true contamination, concrete samples are collected and analyses are performed at several locations within the premises. These two types of measurement are first dealt separately, then cokriging techniques are applied to estimate the contamination over the premise, taking both information into account. This paper presents a methodological study of geostatistical and computational approaches to target suitable areas for additional radiological measures. In order to compare the proposed augmented sampling designs, several optimization criteria are taken into account. Their diversity ensures the coverage of a wide range of real problematics. Two algorithms (greedy algorithm and simulated annealing) are developed to optimize the chosen criterion value as a function of the location of the additional points. The sampling scenarios obtained with the different algorithms are compared in terms of optimization performance and computational efficiency. © 2013 Springer-Verlag Berlin Heidelberg.
Bourges M.,Geovariances |
Mari J.-L.,French Institute of Petroleum |
Geophysical Prospecting | Year: 2012
Nowadays, geostatistics is commonly applied for numerous gridding or modelling tasks. However, it is still under used and unknown for classical geophysical applications. This paper highlights the main geostatistical methods relevant for geophysical issues, for instance to improve the quality of seismic data such as velocity cubes or interpreted horizons. These methods are then illustrated through four examples. The first example, based on a gravity survey presents how a geostatistical interpolation can be used to filter out a global trend, in order to better define real anomalies. In the second case study, dedicated to refraction surveying, geostatistical filtering is used to filter out acquisition artefacts and identify the main geological structures. The third one is an example of porosity being integrated geostatistically with a seismic acoustic impedance map. The last example deals with geostatistical time to depth conversion; the interest of performing geostatistical simulations is finally discussed. © 2012 European Association of Geoscientists & Engineers.
Pernet F.,French Research Institute for Exploitation of the Sea |
Pernet F.,IRD Montpellier |
Lagarde F.,French Research Institute for Exploitation of the Sea |
Jeanne N.,Geovariances |
And 5 more authors.
PLoS ONE | Year: 2014
Although spatial studies of diseases on land have a long history, far fewer have been made on aquatic diseases. Here, we present the first large-scale, high-resolution spatial and temporal representation of a mass mortality phenomenon cause by the Ostreid herpesvirus (OsHV-1) that has affected oysters (Crassostrea gigas) every year since 2008, in relation to their energetic reserves and the quality of their food. Disease mortality was investigated in healthy oysters deployed at 106 locations in the Thau Mediterranean lagoon before the start of the epizootic in spring 2011. We found that disease mortality of oysters showed strong spatial dependence clearly reflecting the epizootic process of local transmission. Disease initiated inside oyster farms spread rapidly beyond these areas. Local differences in energetic condition of oysters, partly driven by variation in food quality, played a significant role in the spatial and temporal dynamics of disease mortality. In particular, the relative contribution of diatoms to the diet of oysters was positively correlated with their energetic reserves, which in turn decreased the risk of disease mortality. © 2014 Pernet et al.
Birgand F.,North Carolina State University |
Faucheux C.,Geovariances |
Gruau G.,French National Center for Scientific Research |
Augeard B.,IRSTEA |
And 2 more authors.
Transactions of the ASABE | Year: 2010
The objectives of this study are to evaluate the uncertainty in annual nitrate loads and concentrations (such as annual average and median concentrations) as induced by infrequent sampling and by the algorithms used to compute fluxes. A total of 50 watershed-years of hourly to daily flow and concentration data gathered from nine watersheds (5 to 252 km 2) in Brittany, France, were analyzed. Original (high frequency) nitrate concentration and flow data were numerically sampled to simulate common sampling frequencies. Annual fluxes and concentration indicators calculated from the simulated samples were compared to the reference values calculated from the high-frequency data. The uncertainties contributed by several algorithms used to calculate annual fluxes were also quantified. In all cases, uncertainty increased as sampling intervals increased. Results showed that all the tested algorithms that do not use continuous flow data to compute nitrate fluxes introduced considerable uncertainty. The flow-weighted average concentration ratio method was found to perform best across the 50 annual datasets. Analysis of the bias values suggests that the 90th and 95th percentiles and the maximum concentration values tend to be systematically underestimated in the long term, but the load estimates (using the chosen algorithm) and the average and median concentrations were relatively unbiased. Great variability in the precision of the load estimation algorithms was observed, both between watersheds of different sizes and between years for a particular watershed. This has prevented definitive uncertainty predictions for nitrate loads and concentrations in this preliminary work, but suggests that hydrologic factors, such as the watershed hydrological reactivity, could be a key factor in predicting uncertainty levels. © 2010 American Society of Agricultural and Biological Engineers ISSN 2151-0032.
Meunier R.,Geovariances |
Binet H.,Geovariances |
Petroleum Geostatistics 2015 | Year: 2015
Factorial kriging or kriging with filtering (Matheron, 1982) is used on post-stack or pre-stack seismic dataset to filter out unwanted components from the seismic signal. To account for non-stationarity that is often encountered within seismic data sets, kriging parameters can be locally set using Local Geo-Statistics (LGS) or M-GS (Moving-GeoStatistics) (Magneron, 2009). There are several approaches to compute the optimized parameters; local variogram parameters in adjacent areas, automatic crossvalidation techniques and morphological analysis. The paper focuses on the latter approach. The idea is to determine some interesting characteristics of a seismic image that should then be transformed to local kriging parameters for the variogram model and the neighbourhood extension. Mathematical morphology techniques provide a set of tools to analyse the image, however they are not well known by geophysicists who are more familiar with seismic attributes. A seismic attribute is a quantity extracted or derived from seismic data that can be analysed in order to enhance information of a seismic image. The advantage of using seismic attributes is that they are available on common geophysical interpretation software packages. Surprisingly there is no much reference of the application of such attributes to derive the local parameters of the geostatistical filters.
Petroleum Geostatistics 2015 | Year: 2015
There are several ways for integrating different sources of data in mapping processes: Multivariate estimations (cokriging, collocated cokriging) which require the fitting of a multivariate model (variograms and cross-variograms) and a stationary context; Kriging with external drift or kriging with Bayesian drift, which can be applied in non-stationary contexts and requires a univariate model. This paper proposes another approach which is based on the definition of additional data, well distributed over the area of interest, which define upper and lower envelopes for the map to be drawn. These envelopes are built from auxiliary data and will be considered as soft data of less accuracy than hard data. The approach is based on the combination of two geostatistical methods that are quite rarely used, conditional expectation with inequalities and kriging with measurement error. After a brief reminder of the methods, some applications in geological modelling are proposed: Control of extrapolation Mapping of geological horizons using the full trace of horizontal wells Integration of geophysical data of varying accuracy in mapping at regional scale Mapping layer tops in layer-cake models.
Bessin C.,Geovariances |
Deraisme J.,Geovariances |
Renard D.,MINES ParisTech
Transactions of the Institutions of Mining and Metallurgy, Section B: Applied Earth Science | Year: 2015
Choosing a resource estimation approach for uranium deposits central Jordan needs to consider various issues; the particular geological context of these deposits, the varying degree of reliability of input data and the level of selectivity that can be reasonably envisaged at a production stage. These issues make this resource estimation challenging from a geostatistical perspective. Here, we provide details of the approach used during resource estimation for the surficial part of uranium deposits in central Jordan; as a more standard approach has been applied to the deeper parts of these deposits. The workflow is as follows: (i) Interpolation of the geometry of the mineralised formation. Kriging with external drift is applied to model hangingwall and footwall surfaces. (ii) Estimation of global resources by 2D estimation of layer thicknesses and uranium accumulation using channel samples within delineated areas. (iii) Accounting for vertical selectivity and development of grade tonnage curves using uniform conditioning (UC) followed by localised post-processing (called LUC) delivering, a 3D block model at the selective mining unit support scale. A description of the UC/LUC approach and the adaptations made in order to account for the variable thickness is presented in this paper. This approach involves performing UC on each panel in turn with a thickness varying from panel to panel. This leads to a specific change of support coefficients for each panel. The illustrations of this approach are taken from one specific zone within the Central Jordan deposits. © 2015 Institute of Materials, Minerals and Mining and The AusIMM.
Bourges M.B.,Geovariances |
75th European Association of Geoscientists and Engineers Conference and Exhibition 2013 Incorporating SPE EUROPEC 2013: Changing Frontiers | Year: 2013
Geomodelling aims at representing as well as possible the reservoir heterogeneities in terms of lithofacies and petrophysical variables. Consequently, the modelling methods and their optimization play an important role for a refined and adequate modelling. When modelling, the issues to tackle are, among others: the level of realism of the facies simulations, the conditioning of simulations to wells data and the spatial behavior (local anisotropy integration for instance). Several simulations methods are available either in geological modelling or in petrophysical modelling. All those methods have their pros and cons. Multiple-points Statistics simulations (MPS), at the edge between pixel-based and object-based methods, may look more promising but needs reliable and consistent prior information for the facies distribution and relationships (geological training image). This paper aims at combining several algorithms in order to benefit from their advantages and therefore optimize the modelling. The following workflow is proposed: first, a process-based algorithm for Meandering Channels simulations is run in order to generate realistic facies simulations. This geological information is then used as a training image for MPS simulations, allowing efficient data conditioning. Finally, locally varying anisotropies are integrated for ensuring a better continuity of porosity simulations in the main direction of each channel. Copyright © (2012) by the European Association of Geoscientists & Engineers All rights reserved.
Bertoli O.,Geovariances Pty Ltd |
Paul A.,BHP Billiton |
Casley Z.,Geovariances |
Dunn D.,BHP Billiton
International Journal of Coal Geology | Year: 2013
Geostatistical drill hole spacing analysis ('DHSA') for resource classification using the global estimation variance technique has been used across BHP Billiton Mitsubishi Alliance ('BMA') Coal Operation's various mines and projects since 2004. Analysis of the results points to the emergence of possible patterns in the results for projects pertaining to specific coal measures being mined by BMA. This correlation may be a useful guide to assist in developing resource classifications for projects based on the coal measures in which they occur. Comparison of the results of classification using the Coal Guidelines versus classification using the geostatistical DHSA method for a selection of BMA's operating mines in Queensland's Bowen Basin indicates that the non-geostatistical approach leads to level of uncertainty that does not always agree with the complexity of the geology. © 2013 Elsevier B.V.
Desnoyers Y.,Geovariances |
Chiles J.-P.,MINES ParisTech |
Dubot D.,CEA Fontenay-aux-roses |
Jeannee N.,Geovariances |
Idasiak J.-M.,CEA Marcoule Nuclear Site
Stochastic Environmental Research and Risk Assessment | Year: 2011
Geostatistics applied to radiological evaluation of nuclear premises provides sound methods to estimate radiological activities, together with their uncertainty. Quantification and risk analysis of contaminated areas are initially performed by applying geostatistical methods relying on the multi-Gaussian assumption. However, the application of the classical bi-Gaussian model for disjunctive kriging proves sub-optimal due to the spatial structuring of high and low values. The beta model which pertains to the class of Hermitian isofactorial models is potentially better suited to radiological evaluation as it allows a continuous evolution from a mosaic to a pure diffusive model. In the test case, disjunctive kriging estimates are obtained by applying in turn the beta model and the pure diffusive model. The comparison of estimation outcomes shows rather limited differences, primarily located in and around the homogeneous contaminated areas. © 2011 Springer-Verlag.