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This article combining geophysics and archaeology aims to provide a more comprehensive characterization of the La Ruchelle valley located in the south of the Celtic site of La Peyrouse (Saint‐Félix‐de‐Villadeix, Dordogne, France) (occupied between the 3rd century BC and the 2nd century AD) through geological prospection (core sampling, geotechnical prospection and mechanical prospection) and near‐surface geophysics (electrical resistivity tomography). On this site, other studies have been carried out since the discovery of the site, such as magnetic prospecting and a hydrogeological study. Geophysical data coupled with geological prospection were used to understand the geology of the valley: (1) The bottom of the valley is filled with colluvium with a thickness of 6 m maximum; (2) part of the colluvium filling characterize by a very low resistivity is indeed archaeological remains; (3) three very distinct geological horizons have been detected—two of these horizons are characteristic of Campanian limestone (C6d and C6e), and the last consists of colluvium with a filling between 2 and 6 m in the centre of the valley, which is particularly thick. This last very thick formation is in fact filled with archaeological and prehistoric remains. [ABSTRACT FROM AUTHOR]

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]

The Chauvet-Pont d'Arc Cave (Ardèche, France), famous for its remarkable rock art, also contains unique thermal-alterations such as rock spalling and color changes on the walls. These alterations resulted from intense fires that have not been observed in the other decorated caves thus far discovered. The functions of these unusual fires challenge archaeologists. To characterize these combustions, we used a numerical tool, previously validated with experimental data, to study the thermo-alterations in the Megaceros Gallery. This unprecedented approach in cave art research enabled us to assess the wood quantities and locations of the hearths responsible for the thermo-alterations. We report here that at least ten fires took place in the Megaceros Gallery while burning more than 170 kg of wood. Both simulation and in situ observations suggest that the branches were arranged in a tepee shape and purposefully positioned, some distance from the walls. This method therefore enables further analysis of the functions of these fires. [ABSTRACT FROM AUTHOR]

In 1994, three speleologists discovered the Chauvet–Pont d'Arc Cave, which contains singular thermal marks on walls deep in the cavity. These alterations arose from intense fires, and understanding their characteristics would help archaeologists suggest hypotheses about the function of such activities. In this context, three confined fires were conducted in a former underground quarry to reproduce thermo-alterations similar to those in the Chauvet–Pont d'Arc Cave and extract experimental data. Each fire involved approximately 135 kg of wood, which was continuously supplied by firemen for safety reasons (> 500°C) and burnt in the shape of a tepee 80 cm in diameter for 50 min. This paper presents the validation of a numerical model on this experimentation. The modelling requires coupling between the combustion and wall impact simulations. Thus, a link between the combustion code FireFOAM and the thermo-mechanical code Cast3m was created with Python scripts. The results from the simulation agree with the measurements and the observations. More specifically, the analysis is based on the temperatures, gas and particle concentrations, gas velocities, soot deposition, colour changes at the walls and areas likely to spall. These data were collected from thirty-seven measuring points covering the whole quarry. This validated tool will provide information about the features of the fires that occurred within the Chauvet–Pont d'Arc Cave. [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]

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 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 The lower terraces of the Dordogne River at Bergerac (south-west France) were studied in detail using cores, trenches, ground penetrating radar profiles, and 14C, optically stimulated luminescence and infrared stimulated luminescence dating. This study shows that the lowest terrace (Fx) is made up of two major lithostratigraphic units: (i) dominantly horizontal gravel strata interpreted as compound bar deposits in a braided river, which pre-date 18-17 ka, and (ii) thick lateral accretion gravel units (point bars) formed in an anabranching river during the Late Pleniglacial and Lateglacial. Most of the anabranching channels were abandoned at ca. 11-10 ka following shrinkage of the river bed into a single meandering channel and were partially filled by gyttjas during the Boreal chronozone. Finally, the channels were plugged by overbank fines until about 5 ka. Floods seldom reached the Fx terrace during the late Holocene and accretion became negligible. The main phase of bedrock incision spanned the Holocene, whereas the period of channel adjustment to change in river regime during late Marine Isotope Stage (MIS) 2 only led to a redistribution of the Pleniglacial gravels. The higher terrace (Fw3) formed before the Eemian and was covered by colluvial fans, mainly during MIS 2 and 3. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Non-destructive techniques (NDT) are often seen as a practical and efficient way to assess the material and structural condition of existing reinforced concrete structures. However, assessment cannot be reduced to measurement and interpretation, and asset managers and structural engineers often need a quantitative assessment. A combination of several techniques can offer precious help. This article intends to show what kind of improvement can be expected from such a combination. Examples are taken from a series of on-site case studies and laboratory experiments. The focus is on the assessment of water content and concrete porosity as these material properties are key factors regarding their present condition as well as their durability in an aggressive marine environment. Material variability, uncertainties on measurements and model error condition the accuracy of the parameter estimation. Their relative contribution to the quality of material assessment is analysed. [ABSTRACT FROM PUBLISHER]