Surface-wave dispersion inversion is growing in popularity for geotechnical applications, due to its noninvasive character, relative straightforward field procedures and interpretation, especially when the subsurface structure is locally assumed to be one-dimensional (1D). Here, laser-Doppler physical modeling of surface-wave propagation is used to address issues of surface-wave depth penetration, the presence of dipping layers, and the associated limitations and systematic errors propagated in conventional 1D surface-wave inversion. Flat-layered models show that, with an active source and linear spread, the maximum resolvable wavelength of the Rayleigh-wave fundamental mode is on the order of 40% of the spread length. Linearised inversions confirm the rule of thumb that the depth penetration is 20–25% of the spread length, and that correct a priori layer interface depths from refraction analysis allow more accurate results. However, even under optimal conditions, failing to account for a dominant higher mode at low frequency when a stiff shallow layer is present, causes an overestimate of deeper layer shear-wave velocity. Moreover, a layer dip of only a few degrees can significantly bias the surface-wave inversion. If the incorrect a priori information from a single-shot refraction analysis is incorporated in the inverse problem, estimated interface depth depends on the shot position and deeper layer shear-wave velocity is underestimated. Even if correct a priori constraints are used, an underestimate of half-space shear-wave velocity of up to 25% remains.
Levee, dike and earth embankment dam structures are difficult to assess because of their length and complexity. Managers often include geophysical investigations in the overall dike condition assessment and the DC-Electrical Resistivity Imaging (ERI) method is particularly applicable owing to its costeffectiveness and its potential sensitivity to internal erosion. However, due to the truly 3D nature of embankment dikes, implementing inline longitudinal tomographies along with conventional 2D inversion is likely to yield image artifacts. 3D effects from external causes (geometry, water reservoir) can be predicted and therefore we present a new approach based on redefining the normalisation principle to derive apparent resistivities from the measured data. The aim is to provide a set of pre-processed apparent resistivities that are not contaminated by external 3D effects and that yield more reliable results when processed within a 2D conventional inversion scheme. The presented approach is successfully applied to synthetic and real data sets, proving superior to the conventional 2D approach, although data acquisition
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