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Within geoelectrical imaging, the choice of measurement configurations and electrode locations is known to control the image resolution. Previous work has shown that optimized survey designs can provide a model resolution that is superior to standard survey designs. This paper demonstrates a methodology to optimize resolution within a target area, while limiting the number of required electrodes, thereby selecting optimal electrode locations. This is achieved by extending previous work on the 'Compare-R' algorithm, which by calculating updates to the resolution matrix optimizes the model resolution in a target area. Here, an additional weighting factor is introduced that allows to preferentially adding measurement configurations that can be acquired on a given set of electrodes. The performance of the optimization is tested on two synthetic examples and verified with a laboratory study. The effect of the weighting factor is investigated using an acquisition layout comprising a single line of electrodes. The results show that an increasing weight decreases the area of improved resolution, but leads to a smaller number of electrode positions. Imaging results superior to a standard survey design were achieved using 56 per cent fewer electrodes. The performance was also tested on a 3-D acquisition grid, where superior resolution within a target at the base of an embankment was achieved using 22 per cent fewer electrodes than a comparable standard survey. The effect of the underlying resistivity distribution on the performance of the optimization was investigated and it was shown that even strong resistivity contrasts only have minor impact. The synthetic results were verified in a laboratory tank experiment, where notable image improvements were achieved. This work shows that optimized surveys can be designed that have a resolution superior to standard survey designs, while requiring significantly fewer electrodes. This methodology thereby provides a means for improving the efficiency of geoelectrical imaging.

Landslides pose significant risks to communities and infrastructure, and mitigating these risks relies on understanding landslide causes and triggering processes. It has been shown that geophysical surveys can significantly contribute to the characterization of unstable slopes. However, hydrological processes can be temporally and spatially heterogeneous, requiring their related properties to be monitored over time. Geoelectrical monitoring can provide temporal and volumetric distributions of electrical resistivity, which are directly related to moisture content. To date, studies demonstrating this capability have been restricted to 2-D sections, which are insufficient to capture the full degree of spatial heterogeneity. This study is the first to employ 4-D (i.e., 3-D time lapse) resistivity imaging on an active landslide, providing long-term data (3 years) highlighting the evolution of moisture content prior to landslide reactivation and showing its decline post reactivation. Crucially, the time-lapse inversion methodology employed here incorporates movements of the electrodes on the unstable surface. Although seasonal characteristics dominate the shallow moisture dynamics during the first 2 years with surficial drying in summer and wetting in winter, in the months preceding reactivation, moisture content increased by more than 45% throughout the slope. This is in agreement with independent data showing a significant rise in piezometric heads and shallow soil moisture contents as a result of prolonged and intense rainfall. Based on these results, remediation measures could be designed and early-warning systems implemented. Thus, resistivity monitoring that can allow for moving electrodes provides a new means for the effective mitigation of landslide risk.

Measurement sensors permanently installed on landslides will inevitably change their position over time due to mass movements. To interpret and correct the recorded data, these movements have to be determined. This is especially important in the case of geoelectrical monitoring, where incorrect sensor positions produce strong artefacts in the resulting resistivity models. They may obscure real changes, which could indicate triggering mechanisms for landslide failure or reactivation. In this paper we introduce a methodology to interpolate movements from a small set of sparsely distributed reference points to a larger set of electrode locations. Within this methodology we compare three interpolation techniques, i.e., a piecewise planar, bi-linear spline, and a kriging based interpolation scheme. The performance of these techniques is tested on a synthetic and a real-data example, showing a recovery rate of true movements to about 1% and 10% of the electrode spacing, respectively. The significance for applying the proposed methodology is demonstrated by inverse modelling of 4D electrical resistivity tomography data, where it is shown that by correcting for sensor movements corresponding artefacts can virtually be removed and true resistivity changes be imaged.

Fracture curvature has been observed from the millimetre to the kilometre scales. Nevertheless, characterizing curvature remains challenging due to data sparsity and geometric ambiguities. As a result, most numerical models often assume planar fractures to ease computations. To address this limitation, we present a novel approach for inferring fracture geometry from travel‐time data of electromagnetic or seismic waves. Our model utilizes co‐kriging interpolation of control points in a three‐dimensional surface mesh to simulate fracture curvature effectively, resulting in an unstructured triangular grid. We then refine the fracture surface into a structured grid with equidistant elements so that both small‐scale heterogeneities and large‐scale curvature can be modelled. To constrain the fracture geometry, we perform a deterministic travel‐time inversion to optimally place these control points. We validate our methodology with synthetic data and address its limitations. Finally, we infer the geometry of a large (more than 200 m) fracture observed in single‐hole ground‐penetrating radar field data. The fracture surface closely agrees with borehole televiewer observations and is also constrained far from the boreholes. Our modelling approach can be trivially adapted to multi‐offset ground‐penetrating radar or active seismic data. [ABSTRACT FROM AUTHOR]

We present compact reverse time migration (CRTM), a real-time ultrasound imaging method that can exploit the full waveform information of ultrasonic wave records for imaging breast tissue. Conventional reverse time migration (RTM) computes the gradient of the reflective ultrasound data with respect to the perturbation of the velocity model of the soft tissues and the gradient can indicate the interface between different types of body tissue. In contrast to conventional reflection ultrasound (B-mode), which is based on the high-frequency approximation to the wave equation, the RTM algorithm is based on the complete wave equation, and can thus exploit the full waveform (wide-spectrum) information of the data and provide an image with higher resolution. Unfortunately, the computational burden of RTM is noticeably higher than the ray-based B-mode. This precludes real-time applications, one of the most important features of ultrasound imaging. The proposed CRTM algorithm can significantly reduce the computational costs of RTM, such that it can be applied for real-time imaging. We demonstrate the performance of CRTM through a synthetic experiment of ultrasound breast imaging. CRTM can be potentially adapted to related signal-processing fields, such as seismic imaging, acoustic camera systems, and radar imaging. [ABSTRACT FROM AUTHOR]