Studies assembling high quality datasets of fracture systems (joints and faults) from four reservoir analogues are described. These comprise limestones (Ireland), sandstones (Norway and Saudi Arabia) and chalk (Denmark). These are used with existing information from the literature to review the major controls and scaling behaviour of fracture systems expected in reservoir rocks. Lithological layering was found to be important and two end-member fracture systems have been identified. In "stratabound" systems, fractures are confined to single layers, sizes are scale restricted, and spacing is regular. In "non-stratabound systems", fractures show a wide range of sizes (often power-law), are spatially clustered and vertically persistent. In nature, variations between and combinations of these systems exist. These end-member systems have contrasting implications for fluid flow, including the scale of fracture that controls flow and the existence of a representative elementary volume, and thus on appropriate modelling approaches.

Uncertainty is a major aspect of the estimation, using models, of the risk of human exposure to pollutants. The Monte Carlo method, which applies probability theory to address model parameter uncertainty, relies on a statistical representation of available information. In recent years, the theory of possibilities has been proposed as an alternative approach to address model parameter uncertainty in situations where available information are insufficient to identify statistically representative probability distributions, due in particular to data scarcity. In practice, it may occur that certain model parameters can be reasonably represented by probability distributions, because there is sufficient data available to substantiate such distributions by statistical analysis, while others are better represented by fuzzy numbers (due to data scarcity). The question then arises as to how these two modes of representation of model parameter uncertainty can be combined for the purpose of estimating the risk of exposure. In this paper an approach (termed a hybrid approach) for achieving such a combination is proposed, and applied to the estimation of human exposure, via vegetable consumption, to cadmium present in the surficial soils of an industrial site located in the north of France. The application illustrates the potential of the proposed approach, which allows the uncertainty affecting model parameters to be represented in a fashion which is consistent with the information at hand.

International audienceGeothermal anomalies in sedimentary basins are strongly controlled by fluid circulation within permeable zones. This study presents a new compilation of newly corrected bottom-hole temperature data in the French part of the Upper Rhine Graben, where the Soultz-sous-Forêts temperature anomaly is locked beneath a local horst structure. After a geostatistically constrained interpolation procedure, maps and cross-sections are extracted from the 3D thermal block, together with the associated standard deviations. Thermal anomalies are preferentially associated with the thickest zones of the permeable fractured Buntsandstein (sandstones) formation, in apparent contradiction with previous models where two major fault zones were suggested to control fluid flow. The underlying fractured granitic basement hosts fluid circulation patterns which are apparently controlled at large-scale by the inclined basement-sediments interface. Based on these observations, numerical models of hydrothermal convection including an inclined basement-sediments interface, a local horst structure, and realistic petrophysical properties have been carried out. The depth-decrease of permeability, the inclination of the interface and the fixed heat flow condition at the base of the model, explain why only a few upwellings can be triggered. Thermal anomalies and a measured temperature profile can be reproduced when fault permeability equals 10-14 m². Interestingly, structure of convective patterns also exhibits a second hotter upwelling, 8 km east of the Soultz-sous-Forêts upwelling zone, where another geothermal exploration project is now underway. The understanding of hydrothermal convection with realistic fluid and rock properties clearly appears as a predictive tool for geothermal exploration strategie

This article presents the main results of the Persistent Scatterer Interferometry Codes Cross Comparison and Certification for long term differential interferometry (PSIC4) project. The project was based on the validation of the PSI (Persistent Scatterer Interferometry) data with respect to levelling data on a subsiding mining area near Gardanne, in the South of France. Eight PSI participant teams processed the SAR data without any a priori information, as a blind test. Intercomparison of the different teams' results was then carried out in order to assess any similarities and discrepancies. The subsidence velocity intercomparison results obtained from the PSI data showed a standard deviation between 0.6 and 1.9 mm/year between the teams. The velocity validation against rates measured on the ground showed a standard deviation between 5 and 7 mm/year. A comparison of the PSI time series and levelling time series shows that if the displacement is larger than about 2 cm in between two consecutive SAR-images, PS-InSAR starts to seriously deviate from the levelling time series. Non-linear deformation rates up to several cm/year appear to be the main reason for these reduced performances, as no prior information was used to adjust the processing parameters. Under such testing conditions and without good ground-truth information, the phase-unwrapping errors for this type of work are a major issue. This point illustrates the importance of having ground truth information and a strong interaction with the end-user of the data, in order to properly understand the type and speed of the deformation that is to be measured, and thus determine the applicability of the technique.

Estimating forest canopy height from large-footprint satellite LiDAR waveforms is challenging given the complex interaction between LiDAR waveforms, terrain, and vegetation, especially in dense tropical and equatorial forests. In this study, canopy height in French Guiana was estimated using multiple linear regression models and the Random Forest technique (RF). This analysis was either based on LiDAR waveform metrics extracted from the GLAS (Geoscience Laser Altimeter System) spaceborne LiDAR data and terrain information derived from the SRTM (Shuttle Radar Topography Mission) DEM (Digital Elevation Model) or on Principal Component Analysis (PCA) of GLAS waveforms. Results OPEN ACCESSRemote Sens. 2014, 6 11884show that the best statistical model for estimating forest height based on waveform metrics and digital elevation data is a linear regression of waveform extent, trailing edge extent, and terrain index (RMSE of 3.7 m). For the PCA based models, better canopy height estimation results were observed using a regression model that incorporated both the first 13 principal components (PCs) and the waveform extent (RMSE = 3.8 m). Random Forest regressions revealed that the best configuration for canopy height estimation used all the following metrics: waveform extent, leading edge, trailing edge, and terrain index (RMSE = 3.4 m). Waveform extent was the variable that best explained canopy height, with an importance factor almost three times higher than those for the other three metrics (leading edge, trailing edge, and terrain index). Furthermore, the Random Forest regression incorporating the first 13 PCs and the waveform extent had a slightly-improved canopy height estimation in comparison to the linear model, with an RMSE of 3.6 m. In conclusion, multiple linear regressions and RF regressions provided canopy height estimations with similar precision using either LiDAR metrics or PCs. However, a regression model (linear regression or RF) based on the PCA of waveform samples with waveform extent information is an interesting alternative for canopy height estimation as it does not require several metrics that are difficult to derive from GLAS waveforms in dense forests, such as those in French Guiana.