S U M M A R YNew techniques for improving both the computational and imaging performance of the threedimensional (3-D) electromagnetic inverse problem are presented. A non-linear conjugate gradient algorithm is the framework of the inversion scheme. Full wave equation modelling for controlled sources is utilized for data simulation along with an efficient gradient computation approach for the model update. Improving the modelling efficiency of the 3-D finite difference (FD) method involves the separation of the potentially large modelling mesh, defining the set of model parameters, from the computational FD meshes used for field simulation. Grid spacings and thus overall grid sizes can be reduced and optimized according to source frequencies and source-receiver offsets of a given input data set. Further computational efficiency is obtained by combining different levels of parallelization. While the parallel scheme allows for an arbitrarily large number of parallel tasks, the relative amount of message passing is kept constant. Image enhancement is achieved by model parameter transformation functions, which enforce bounded conductivity parameters and thus prevent parameter overshoots. Further, a remedy for treating distorted data within the inversion process is presented. Data distortions simulated here include positioning errors and a highly conductive overburden, hiding the desired target signal. The methods are demonstrated using both synthetic and field data.

A parallel finite‐difference algorithm for the solution of diffusive, three‐dimensional (3D) transient electromagnetic field simulations is presented. The purpose of the scheme is the simulation of both electric fields and the time derivative of magnetic fields generated by galvanic sources (grounded wires) over arbitrarily complicated distributions of conductivity and magnetic permeability. Using a staggered grid and a modified DuFort‐Frankel method, the scheme steps Maxwell's equations in time. Electric field initialization is done by a conjugate‐gradient solution of a 3D Poisson problem, as is common in 3D resistivity modeling. Instead of calculating the initial magnetic field directly, its time derivative and curl are employed in order to advance the electric field in time. A divergence‐free condition is enforced for both the magnetic‐field time derivative and the total conduction‐current density, providing accurate results at late times. In order to simulate large realistic earth models, the algorithm has been designed to run on parallel computer platforms. The upward continuation boundary condition for a stable solution in the infinitely resistive air layer involves a two‐dimensional parallel fast Fourier transform. Example simulations are compared with analytical, integral‐equation and spectral Lanczos decomposition solutions and demonstrate the accuracy of the scheme.

S U M M A R YThe growing use of the controlled-source electromagnetic method (CSEM) and magnetotellurics (MT) for exploration applications has been driving the development of data acquisition technologies, and three-dimensional (3-D) modelling and imaging techniques. However, targeting increasingly complex geological environments also further enhances the problems inherent in large-scale inversion, such as non-uniqueness and resolution issues. In this paper, we report on two techniques to mitigate these problems. We use 3-D joint CSEM and MT inversion to improve the model resolution. To avoid the suppression of the resolution capacities of one data type, and thus to balance the use of inherent, and ideally complementary information content, different data reweighting schemes are proposed. Further, a hybrid model parametrization approach is presented, where traditional cell-based model parameters are used simultaneously within a parametric inversion. The idea is to limit the non-uniqueness problem, typical for 3-D imaging problems, in order to allow for a more focusing inversion. The methods are demonstrated using synthetic data generated from models with a strong practical relevance.Large-scale inverse problems are usually underdetermined, meaning that there are more unknowns, typically in the form of highly digitized model meshes, than data. This adds to the problem that errors are associated with every geophysical datum. The resulting issue is referred to as the problem of non-uniqueness of inverse solutions. To mitigate this problem and to improve the resolution in an inversion, it is common to take advantage of complementary natures of different geophysical data sets. In electromagnetic problems, magnetotelluric (MT) data usually provides the conductivity information on a more gross scale, while controlled-source electromagnetic methods (CSEM) have a better ability of illuminating rather subtle targets, particularly thin resistors. MT data have been successfully combined with time-domain CSEM data to invert for one-dimensionally layered models (Hui-Ping et al. 1996;Meju 1996;Rovetta et al. 2008). With CSEM data responding stronger to thin resistive targets, the combination with MT data has a strong relevance for providing a less ambiguous interpretation of data measured over hydrocarbon prospects. Mackie et al. (2007) report a proof of concept showing the improved resolution by combining marine CSEM and MT synthetic data for mapping thin resistors.Even with improved resolution capabilities, the solutions of 3-D large-scale cell-based (or pixel-based) inversions with finely sampled models usually remain non-unique. Several strategies have been reported to limit the ambiguities for reconstructed targets and its conductivities. For cell-based problems, model-smoothing constraints are commonly applied, limiting the solutions to a class of geologically more meaningful ones, that is, avoiding conductivity variations that are unphysical. A different approach is to actually address the underdeterminacy by casting the...

Formation anisotropy should be incorporated into the analysis of controlled source electromagnetic (CSEM) data because failure to do so can produce serious artifacts in the resulting resistivity images for certain data configurations of interest. This finding is demonstrated in model and case studies. Sensitivity to horizontal resistivity will be strongest in the broadside electric field data where detectors are offset from the tow line. Sensitivity to the vertical resistivity is strongest for over flight data where the transmitting antenna passes directly over the detecting antenna. Consequently, consistent treatment of both over flight and broadside electric field measurements requires an anisotropic modeling assumption. To produce a consistent resistivity model for such data we develop and employ a 3D CSEM imaging algorithm that treats transverse anisotropy. The algorithm is based upon non-linear conjugate gradients and full wave equation modeling. It exploits parallel computing systems to effectively treat 3D imaging problems and CSEM data volumes of industrial size. Here we use it to demonstrate the anisotropic imaging process on model and field data sets from the North Sea and offshore Brazil.We also verify that isotropic imaging of over flight data alone produces an image generally consistent with the vertical resistivity. However, superior data fits are obtained when the same over flight data are analyzed assuming an anisotropic resistivity model. 2