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The goal of data classification is to organize them into relevant groups using algorithms. In this study, two clustering algorithms are applied to classify a set of geophysical measurements performed around the Lascaux Cave (Dordogne, France). Based on a non-destructive geophysical method, electrical resistivity tomography (ERT), the data (resistivity values) are supposed to characterize the rock mass around the cave. The rock mass must be divided into an optimal number of homogeneous domains with specific thermal properties to integrate them in the future thermo-aeraulic simulations of the cave. Since the data are georeferenced, a given resistivity cluster corresponds to a specific spatial domain of the rock mass. This study aims to compare two different clustering methods, the Hierarchical Agglomerative Clustering (HAC) and the K-means methods, on the resistivity data set. Thus, the objective of this study is to determine which of the two methods leads to a partition of the massif with an optimal number of classes, allowing us to find the geological structures partially known thanks to previous studies. The results of these methods are analyzed in light of two different indices, the Silhouette Index (SI) and the Coefficient of Variation (CV). Based on these indices alone, K-means might seem to be the best algorithm, but a fine analysis of each cluster shows that the HAC method gives better results. Indeed, only the HAC method highlights areas already known to be either waterlogged or clayey. Our partial geological knowledge, which is of paramount importance in such a study, also supports the choice of the HAC method. [ABSTRACT FROM AUTHOR]

Water migration within the rock mass during a fire has not yet been studied. Here, an innovative high-resolution micro-ERT method is developed at a centimetric scale to measure this phenomenon. The method is two-step based: first, a correction of the electrode effect, and second, the correction of resistivity for rapid variations in temperature with high values. Water migration is successfully described within the wall of an underground limestone quarry with three phases: from fire ignition to 36 min, the surface dries to a 3-cm depth and a higher saturated layer forms deeper than 0.10 m; from 36 min to 46 min (fire supply stops), the rock dries out and water migrates from deeper parts of the rock wall, deeper than 20 cm, towards the surface; finally, after fire extinction, the water migrates through a readjustment phase until it returns to its initial state. [ABSTRACT FROM AUTHOR]

ABSTRACT In this paper, we present the uncertainty analysis of the 2D electrical tomography inverse problem using model reduction and performing the sampling via an explorative member of the Particle Swarm Optimization family, called the Regressive-Regressive Particle Swarm Optimization. The procedure begins with a local inversion to find a good resistivity model located in the nonlinear equivalence region of the set of plausible solutions. The dimension of this geophysical model is then reduced using spectral decomposition, and the uncertainty space is explored via Particle Swarm Optimization. Using this approach, we show that it is possible to sample the uncertainty space of the electrical tomography inverse problem. We illustrate this methodology with the application to a synthetic and a real dataset coming from a karstic geological set-up. By computing the uncertainty of the inverse solution, it is possible to perform the segmentation of the resistivity images issued from inversion. This segmentation is based on the set of equivalent models that have been sampled, and makes it possible to answer geophysical questions in a probabilistic way, performing risk analysis. [ABSTRACT FROM AUTHOR]

The Lascaux cave, located in southwest France, is one of the most important prehistoric cave in the world that shows Paleolithic paintings. This study aims to characterize the structure of the weathered epikarst setting located above the cave using Time-Lapse Electrical Resistivity Tomography (ERT) combined with local hydrogeological and climatic environmental data. Twenty ERT profiles were carried out for two years and helped us to record the seasonal and spatial variations of the electrical resistivity of the hydraulic upstream area of the Lascaux cave. The 20 interpreted resistivity models were merged into a single synthetic model using a multidimensional statistical method (Hierarchical Agglomerative Clustering). The individual blocks from the synthetic model associated with a similar resistivity variability were gathered into 7 clusters. We combined the resistivity temporal variations with climatic and hydrogeological data to propose a geo-electrical model that relates to a conceptual geological model. We provide a geological interpretation for each cluster regarding epikarst features. The superficial clusters (no 1 & 2) are linked to effective rainfall and trees, probably a fractured limestone. Another two clusters (no 6 & 7) are linked to detrital formations (sand and clay respectively). The cluster 3 may correspond to a marly limestone that forms a non-permeable horizon. Finally, the electrical behavior of the last two clusters (no 4 & 5) is correlated with the variation of flow rate; they may be a privileged feed zone of the flow in the cave. [ABSTRACT FROM AUTHOR]

The Lascaux Cave, located in the south-eastern part of the department of the Dordogne (24, France), is considered to be one of the most important prehistoric caves in the world. The scope of this study is the protection and conservation of the Lascaux paintings from a hydrogeological and climatic environmental standpoint. Geophysical methods enable us to monitor the environment of the decorated cave in a non-invasive way. Electrical resistivity tomography (ERT) using a pole--dipole array, together with analysis of the local effective rainfall (groundwater recharge) and the flow in the cave, helps us to identify an area where upstream underground water is probably stored e.g. a recharge zone. There is a relationship between resistivity change in this zone and the underground flow measured in the cave, but with a time lag. Thus systematic electrical surveys following rainy periods could be used to predict the beginning of the underground flow and in so doing provide valuable advice for the preservation of the Lascaux Cave. [ABSTRACT FROM AUTHOR]

Abstract: In France, the monitoring of landfill cover after closure of the site is a local problem, since its tightness must be ensured over time. Leaks in the cover are a problem, as they allow water to infiltrate the stored waste. In order to locate such leaks, electrical resistivity tomography was used on an experimental site in which defects had been intentionally made in the cover. Repeated measurements taken on this site showed that the weather conditions preceding the measurements need to be taken into account, as they affect the water content in the cover material. They also showed that there are optimal weather conditions for detecting defects in the cover. A statistical analysis carried out on the electrical resistivity results for all surveys and cover material samples showed that the material was heterogeneous; this variability was mainly due to a difference in particle size (fines content) and in compaction. This study has shown the capacity of electrical resistivity tomography to detect defects and heterogeneity in the cover material, indicating that it is a good means of monitoring the quality of landfill cover both when it is put in place and subsequently. [Copyright &y& Elsevier]