This work presents an attempt to monitor water infiltration and subsurface flow within a clay-shale landslide using time-lapse electrical resistivity tomography (ERT). A rainfall experiment was carried out on a plot of 100 m2 at the Laval landslide in the Draix experimental catchments (ORE Draix, South French Alps) in order to characterize the spatial and temporal development of water circulation in the soil and to identify when steady-state flow conditions are reached. The experiment was conducted during 67 h with initial unsaturated conditions in the slope. The apparent electrical resistivity values were inverted with a time-lapse approach using several cross models. The results indicate a significant decrease in resistivity (-18%) compared to the initial state in the rain plot. Downslope progression of negative resistivity anomalies is imaged suggesting that vertical and subsurface lateral flows have developed. About 21 h after the start of the rain experiment, a constant level of resistivity values is observed indicating that the hydrological system reached steady-state flow conditions. This observation is consistent with ground water level observations and chemical tracer analysis. Computed differences in time of steady-state conditions highlight possible preferential flows near the landslide toe. A hydrological concept of functioning of the slope is proposed, and apparent saturated hydraulic conductivity (Ks of 1.7 X 10-4 m X s-1) is computed from the steady-state times. This study demonstrates the potentiality of ERT monitoring to monitor water infiltration in clay-shale slopes and the high water transfer capacity of reworked clay-shale material. Copyright (c) 2011 John Wiley & Sons, Ltd.

A review of the emergence and development of hydrogeophysicsOutline of emerging techniques in hydrogeophysicsPresentation of future opportunities in hydrogeophysics.

In hard-rock aquifers, fractured zones constitute adequate drinking water exploitation areas but also potential contamination paths. One critical issue in hydrogeological research is to identify, characterize, and monitor such fractured zones at a representative scale. A tracer test monitored with surface electrical resistivity tomography (ERT) could help by delineating such preferential flow paths and estimating dynamic properties of the aquifer. However, multiple challenges exist including the lower resolution of surface ERT compared with crosshole ERT, the finite time that is needed to complete an entire data acquisition, and the strong dilution effects. We conducted a natural gradient salt tracer test in fractured limestones. To account for the high transport velocity, we injected the salt tracer continuously for four hours at a depth of 18 m. We monitored its propagation with two parallel ERT profiles perpendicular to the groundwater flow direction. Concerning the data acquisition, we always focused on data quality over temporal resolution. We performed the experiment twice to prove its reproducibility by increasing the salt concentration in the injected solution (from 38 to 154 g/L). Our research focused on how we faced every challenge to delineate a preferential flow and solute transport path in a typical calcareous valley of southern Belgium and on the estimation of the transport velocity (more than 10 m/hour). In this complex environment, we imaged a clear tracer arrival in both ERT profiles and for both tests. Applying filters (with a cutoff on the relative sensitivity matrix and on the background-resistivity changes) was helpful to isolate the preferential flow path from artifacts. Regarding our findings, our approach could be improved to perform a more quantitative experiment. With a higher temporal resolution, the estimated value of the transport velocity could be narrowed, allowing estimation of the percentage of tracer recovery.

AbstractGeophysical monitoring is used principally to interpret the locations and amounts of ground condition changes. To achieve these objectives, differences are computed and examined using time-lapse images calculated under the time-invariant static assumption, that any material property changes during the data measurement can be practically ignored. These monitored data, however, can be contaminated with noise and frequently generate false anomalies of ground condition changes. Furthermore, the assumption of the static model can be invalid if the material property changes significantly during data acquisition. To alleviate these problems, we developed a new least-squares inversion algorithm that allows for the subsurface properties to continuously change in time. We define the subsurface structure and the entire monitoring data in the space–time domain, allowing us to obtain a four-dimensional space–time model using just one inversion process. We introduce the regularizations not only in the space domain but also in time, resulting in reduced inversion artifacts and improved stability of the inverse problem. We demonstrated the performance of the proposed algorithm through numerical experiments that assumed several scenarios of ground condition changes and data acquisition sequences. Finally, the applicability to field data was proven by applying the developed algorithm to the monitoring data of crosshole resistivity tomography jointly performed with a dye tracer flooding experiment. This experiment had a small enough scale that we could not ignore the change of material properties during the data measurement.]]>

Time-lapse monitoring is a powerful tool for observing dynamic changes in the subsurface. In particular it offers the potential for achieving inversion results with increased fidelity through the inclusion of complementary information from multiple time-steps. This inclusion of complementary information can reduce the need for spatial smoothing, without adding inversion artifacts to the resulting images. Commonly used time-lapse inversion methods include the ratio method, cascaded time-lapse inversion, difference inversion and differencing independent inversions. We introduce two additional methods in which both time-lapse data sets are inverted simultaneously. In the first, called temporally constrained time-lapse inversion, inversion of both datasets is done under a single optimization procedure and constraints are added to the regularization to ensure that the changes from one time to another are smooth. In the second method, called simultaneous time-lapse inversion, the inversions at time 1 and time 2 are performed simultaneously and constraints of smoothness and closeness to a reference model are applied to the difference image produced at each iteration, and subsequently, the constraints are updated at each iteration. Through both a numerical and a field example we compare the results of common time-lapse inversion methods as well as the introduced approaches. We found that of the commonly used time-lapse inversion methods the difference inversion method produced the best resolution of time-lapse changes and was the most robust in the presence of noise. However, we found that the alternative approach of simultaneous time-lapse inversion produced the best reconstruction of modeled EC changes in the numerical example and easily interpretable high resolution difference images in the field example. Moreover, there was less tailoring of regularization parameters with our simultaneous time-lapse approach, suggesting that it will lend itself well to an automated inversion code. (C) 2011 Elsevier B.V.

Here the time regularization is not considered to be constant between different time steps but is now allowed to vary depending on the degree of spatial resistivity changes occurring between different monitoring stages. Two methods are proposed to assign different time Lagrangian values, one based on a pre-estimation during execution time, and one using a-priori information. Both methods require a threshold to characterize the significance of the observed resistivity changes with time. We performed numerous numerical experiments using synthetic data to provide reasonable threshold values. Synthetic data tests illustrate that the new algorithm, named 4D Active Time Constrained (4D-ATC), produces in most cases improved time-lapse images when compared with existing techniques. Further the applicability of the new scheme is demonstrated with real data. Overall, the new algorithm is shown to be a useful tool for processing time-lapse resistivity data, which can be used with minor modifications to other types of time-lapse geophysical data. (C) 2010 Elsevier B.V.]]>

Three-dimensional resistivity surveys and their associated inversion models are required to accurately resolve structures exhibiting very complex geology. In the same light, 3D resistivity surveys collected at multiple times are required to resolve temporally varying conditions. In this work we present 3D data sets, both synthetic and real, collected at different times. The large spatio-temporal data sets are then inverted simultaneously using a least-squares methodology that incorporates roughness filters in both the space and time domains. The spatial roughness filter constrains the model resistivity to vary smoothly in the x-, y- and z-directions. A temporal roughness filter is also applied that minimizes changes in the resistivity between successive temporal inversion models and the L-curve method is used to determine the optimum weights for both spatial and temporal roughness filters. We show that the use of the temporal roughness filter can accurately resolve changes in the resistivity even in the presence of noise. The L1- and L2-norm constraints for the temporal roughness filter are first examined using a synthetic model. The synthetic data test shows that the L1-norm temporal constraint produces significantly more accurate results when the resistivity changes abruptly with time. The model obtained with the L1-norm temporal constraint is also less sensitive to random noise compared with independent inversions (i.e., without any temporal constraint) and the L2-norm temporal constraint. Anomalies that are common in models using independent inversions and the L2-norm and L1-norm temporal constraints are likely to be real. In contrast, anomalies present in a model using independent inversions but that are significantly reduced with the L2-norm and L1-norm constraints are likely artefacts. For field data sets, the method successfully recovered temporal changes in the subsurface resistivity from a landfill monitoring survey due to rainwater infiltration, as well as from an experiment to map the migration of sodium cyanide solution from an injection well using surface and borehole electrodes in an area with significant topography.