Comparisons of dry ERT, diachronic ERT and the spectral analysis of surface waves for estimating bedrock depth in various Mediterranean landscapes

Bedrock depth provides important information for many applications, such as groundwater modelling, the estimation of soil water availability and crop production potential. Direct estimates of bedrock depth from destructive soil observations are discontinuous and too expensive to be used in large areas. Geophysical methods are often cited as possible alternatives; however, their ability to provide reliable estimations of bedrock depth relies greatly on local site characteristics. Therefore, this study examines the ability of Electrical Resistivity Tomographie (ERT) in dry conditions, diachronic ERT and the Spectral Analysis of Surface Waves (SASW) method to determine bedrock depth (BD) in different geopedological contexts that are representatives of the Mediterranean landscape (Southern France). SASW was performed using the data that were measured in the field with classical seismic equipment (impulse source and geophones) along a transect in each study site. In the same place, transects of ERT (Wenner-Schlumberger array, 1-m electrode spaced) were measured under wet and dry conditions. To calibrate the geophysical measurements, 131 boreholes (from two to 5 m deep) were interpreted to determine the bedrock depth. Dry ERT, diachronic ERT and SASW were found to have highly variable performances for the estimation of bedrock depth in all geopedological contexts. SASW correctly estimated the bedrock depth (RMSE=0.3-0.7 m) in all situations except in the case of shallow soils. Conversely, dry ERT only estimated bedrock depth (RMSE=0.2 m) in the case of a high contrast of the resistivity between the soil and bedrock. By analysing the pattern of the water uptake by grapevine, diachronic ERT determined the bedrock depth in the case of very low contrasts in resistivity (RMSE=0.5). According to a prior pedological knowledge such soil maps, the combination with the use of a sensor based on different physical parameters may enhance the bedrock detection. (C) 2012 Elsevier B.V. All rights reserved.