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We consider 2D earth models consisting of laterally variable layers. Boundaries between layers are described by their depths at a set of nodes and interpolated laterally between nodes. Conductivity within each layer is described by values at a set of nodes fixed within each layer, and is interpolated laterally within each layer. Within the set of possible models of this sort, we iteratively invert magnetotelluric data for models minimizing the lateral roughness of the layer boundaries, and the lateral roughness of conductivities within layers, for a given level of data misfit. This stabilizes the inverse problem and avoids superfluous detail. This approach allows the determination of boundary positions between geological units with sharp discontinuities in properties across boundaries, while sharing the stability features of recent smooth conductivity distribution inversions. We compare sharp boundary inversion results with smooth conductivity distribution inversion results on a numerical example, and on inversion of field data from the Columbia River flood basalts of Washington State. In the synthetic example, where true positions and resistivities are known, sharp boundary inversion results determine both layer boundary locations and layer resistivities accurately. In inversion of Columbia flood basalt data, sharp boundary inversion recovers a model with substantially less internal variation within units, and less ambiguity in both the depth to base of the basalts and depth to resistive basement.

Accurately estimating reservoir parameters from geophysical data is vitally important in hydrocarbon exploration and production. We have developed a new joint-inversion algorithm to estimate reservoir parameters directly, using both seismic amplitude variation with angle of incidence ͑AVA͒ data and marine controlled-source electromagnetic ͑CSEM͒ data. Reservoir parameters are linked to geophysical parameters through a rock-properties model. Errors in the parameters of the rock-properties model introduce errors of comparable size in the reservoir-parameter estimates produced by joint inversion. Tests of joint inversion on synthetic 1D models demonstrate improved fluid saturation and porosity estimates for joint AVA-CSEM data inversion ͑compared with estimates from AVA or CSEM inversion alone͒. A comparison of inversions of AVA data, CSEM data, and joint AVA-CSEM data over the North Sea Troll field, at a location for which we have well control, shows that the joint inversion produces estimates of gas saturation, oil saturation, and porosity that are closest ͑as measured by the rms difference, the L 1 norm of the difference, and net values over the interval͒ to the logged values. However, CSEM-only inversion provides the closest estimates of water saturation.

Electromagnetic induction data parameterized in time dependent object intrinsic polarizabilities allow discrimination of unexploded ordnance (UXO) from false targets (scrap metal). Data from a cart-mounted system designed for discrimination of UXO with 20 mm to 155 mm diameters are used. Discrimination of UXO from irregular scrap metal is based on the principal dipole polarizabilities of a target. A near-intact UXO displays a single major polarizability coincident with the long axis of the object and two equal smaller transverse polarizabilities, whereas metal scraps have distinct polarizability signatures that rarely mimic those of elongated symmetric bodies. Based on a training data set of known 1 targets, object identification was made by estimating the probability that an object is a single UXO. Our test survey took place on a military base where both 4.2" mortar shells and scrap metal were present. The results show that we detected and discriminated correctly all 4.2" mortars, and in that process we added 7%, and 17%, respectively, of dry holes (digging scrap) to the total number of excavations in two different survey modes. We also demonstrated a mode of operation that might be more cost effective than the current practice.

Electromagnetic induction data parameterized in time dependent object intrinsic polarizabilities allow discrimination of unexploded ordnance (UXO) from false targets (scrap metal). Data from a cart-mounted system designed for discrimination of UXO with 20 mm to 155 mm diameters are used. Discrimination of UXO from irregular scrap metal is based on the principal dipole polarizabilities of a target. A near-intact UXO displays a single major polarizability coincident with the long axis of the object and two equal smaller transverse polarizabilities, whereas metal scraps have distinct polarizability signatures that rarely mimic those of elongated symmetric bodies. Based on a training data set of known 1 targets, object identification was made by estimating the probability that an object is a single UXO. Our test survey took place on a military base where both 4.2" mortar shells and scrap metal were present. The results show that we detected and discriminated correctly all 4.2" mortars, and in that process we added 7%, and 17%, respectively, of dry holes (digging scrap) to the total number of excavations in two different survey modes. We also demonstrated a mode of operation that might be more cost effective than the current practice.

[1] The application of geophysical methods, in particular, electrical resistivity measurements, may be useful for monitoring subsurface contamination. However, interpreting geophysical data without additional data and without considering the associated hydrogeochemical processes is challenging since the geophysical response is sensitive to not only heterogeneity in rock properties but also to the saturation and chemical composition of pore fluids. We present an inverse modeling framework that incorporates the simulation of hydrogeochemical processes and time-lapse electrical resistivity data and apply it to various borehole and cross-borehole data sets collected in 2008 near the S-3 Ponds at the U.S. Department of Energy's Oak Ridge Integrated Field Research Challenge site, where efforts are underway to better understand freshwater recharge and associated contaminant dilution. Our goal is to show that the coupled hydrogeochemical-geophysical modeling framework can be used to (1) develop a model that honors all the available data sets, (2) help understand the response of the geophysical data to subsurface properties and processes at the site, and (3) allow for the estimation of petrophysical parameters needed for interpreting the geophysical data. We present a series of cases involving different data sets and increasingly complex models and find that the approach provides useful information about soil properties, recharge-related transport processes, and the geophysical response. Spatial heterogeneity of the petrophysical model can be described sufficiently with two layers, and its parameters can be estimated concurrently with the hydrogeochemical parameters. For successful application of the approach, the parameters of interest must be sensitive to the available data, and the experimental conditions must be carefully modeled.Citation: Kowalsky, M. B., E. Gasperikova, S. Finsterle, D. Watson, G. Baker, and S. S. Hubbard (2011), Coupled modeling of hydrogeochemical and electrical resistivity data for exploring the impact of recharge on subsurface contamination, Water Resour. Res.,

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Krafla and Hengill volcanic complexes, located 300 km apart, are both known as hightemperature geothermal systems located within neo-volcanic zones of Iceland. This paper demonstrates the utilization of three-dimensional (3D) magnetotelluric (MT) inversions from three different inverse modeling algorithms, which leads to characterizing the electrical resistivity structure of geothermal reservoirs with a much greater level of confidence in accuracy and resolution than if a single algorithm was employed in the data interpretation. These are the first 3D MT inversions of a Krafla MT dataset. The inverted model of electrical resistivity is a classic example of a high-temperature hydrothermal system, with a highly resistive near-surface layer, identified as unaltered porous basalt, overlying a low resistivity cap corresponding to the smectite-zeolite zone. This layer is in turn underlain by a more resistive zone, identified as the epidote-chlorite zone, also called the resistive core, which is often associated with production of geothermal fluids. The electrical structure in the upper 1-2 km does not correlate with lithology but with alteration mineralogy. At the location of the IDDP-1 well, which encountered magma at 2.1 km depth, the resistivity image shows high resistivity, most likely due to the epidote-chlorite geology and/or the presence of deeper supercritical fluids. Two km northwest of the well, however, an intrusive low-resistivity feature is imaged rising from depth, and a plausible interpretation is that of a magma intrusion. One possible explanation for the magma encounter at the IDDP-1 well is the existence of pathways or fissures connected to the magma chamber and intersected by the well. The MT response to these magma pathways is not discernible in the data, perhaps because this magma volume is below the threshold of resolvability. The electrical resistivity structure of the Hengill geothermal area also reveals characteristic features of a high temperature geothermal system with two low-resistivity layers. The nature of the uppermost low-3 resistivity layer and the increasing resistivity below it is attributed to hydrothermal mineral alteration, while the nature of the deep low-resistivity layer, centered over the northeast, is not yet well understood. The geothermal system in the northeast area appears to be shallower than the system manifested in the southwest. 3D MT inversions of Krafla and Hengill data sets show that knowledge of the subsurface electrical resistivity contributes substantially to a better understanding of complex geothermal systems.