We present 2.5D fast and rigorous forward and inversion algorithms for deep electromagnetic (EM) applications that include crosswell and controlled-source EM measurements. The forward algorithm is based on a finite-difference approach in which a multifrontal LU decomposition algorithm simulates multisource experiments at nearly the cost of simulating one single-source experiment for each frequency of operation. When the size of the linear system of equations is large, the use of this noniterative solver is impractical. Hence, we use the optimal grid technique to limit the number of unknowns in the forward problem. The inversion algorithm employs a regularized Gauss-Newton minimization approach with a multiplicative cost function. By using this multiplicative cost function, we do not need a priori data to determine the so-called regularization parameter in the optimization process, making the algorithm fully automated. The algorithm is equipped with two regularization cost functions that allow us to reconstruct either a smooth or a sharp conductivity image. To increase the robustness of the algorithm, we also constrain the minimization and use a line-search approach to guarantee the reduction of the cost function after each iteration. To demonstrate the pros and cons of the algorithm, we present synthetic and field data inversion results for crosswell and controlled-source EM measurements.

We have formulated a 3-D inverse solution for the magnetotelluric (MT) problem using the non-linear conjugate gradient method. Finite difference methods are used to compute predicted data efficiently and objective functional gradients. Only six forward modelling applications per frequency are typically required to produce the model update at each iteration. This efficiency is achieved by incorporating a simple line search procedure that calls for a sufficient reduction in the objective functional, instead of an exact determination of its minimum along a given descent direction. Additional efficiencies in the scheme are sought by incorporating preconditioning to accelerate solution convergence. Even with these efficiencies, the solution's realism and complexity are still limited by the speed and memory of serial processors. To overcome this barrier, the scheme has been implemented on a parallel computing platform where tens to thousands of processors operate on the problem simultaneously. The inversion scheme is tested by inverting data produced with a forward modelling code algorithmically different from that employed in the inversion algorithm. This check provides independent verification of the scheme since the two forward modelling algorithms are prone to different types of numerical error.

A 3D frequency-domain EM modelling code has been implemented for helicopter electromagnetic (HEM) simulations. A vector Helmholtz equation for the electric fields is employed to avoid convergence problems associated with the first-order Maxwell's equations when air is present. Additional stability is introduced by formulating the problem in terms of the scattered electric fields. Vith this formulation the impressed dipole source is replaced with an equivalent source, which for the airborne configuration possesses a smoother spatial dependence and is easier to model. In order to compute this equivalent source, a primary field arising from dipole sources of either a whole space or a layered half-space must be calculated at locations where the conductivity is different from that of the background.The Helmholtz equation is approximated using finite differences on a staggered grid. After finite-differencing, a complex-symmetric matrix system of equations is assembled and preconditioned using Jacobi scaling before it is solved using the quasi-minimum residual (QMR) method. The modelling code has been compared with other lD and 3D numerical models and is found to produce results in good agreement.\7e have used the solution to simulate novel HEM responses that are computationally intractable using integral equation (IE) solutions. These simulations include a 2D conductor residing at a fault contact with and without topography. Our simulations show that the quadrature response is a very good indicator of the faulted background, while the in-phase response indicates the presence of the conductor. However when interpreting the in-phase response, it is possible erroneously to infer a dipping conductor due to the contribution of the faulted background.

Linearized methods are presented for appraising resolution and parameter accuracy in images generated with 2-D and 3-D nonlinear electromagnetic (EM) inversion schemes. When direct matrix inversion is used, the model resolution and a posteriori model covariance matrices can be calculated readily. By analyzing individual columns of the model resolution matrix, the spatial variation of the resolution in the horizontal and vertical directions can be estimated empirically. Plotting the diagonal of the model covariance matrix provides an estimate of how errors in the inversion process, such as data noise and incorrect a priori assumptions, map into parameter error and thus provides valuable information about the uniqueness of the resulting image. Methods are also derived for image appraisal when the iterative conjugate gradient technique is applied to solve the inverse. An iterative statistical method yields accurate estimates of the model covariance matrix as long as enough iterations are used. Although determining the entire model resolution matrix in a similar manner is computationally prohibitive, individual columns of this matrix can be determined. Thus, the spatial variation in image resolution can be determined by calculating the columns of this matrix for key points in the image domain and then interpolating between. Examples of the image analysis techniques are provided on 2-D and 3-D synthetic cross‐well EM data sets as well as a field data set collected at Lost Hills oil field in central California.

[1] Experiments have been conducted to demonstrate the accuracy and precision of moisture content estimates derived from cross-borehole ground penetrating radar (XBGPR) measurements made within the vadose zone. Both numerical simulations and field data demonstrate that although a certain amount of image smearing occurs under ideal conditions the general trends in the spatial variation of the moisture content can be estimated by a simple empirical transformation from images of electromagnetic (EM) wave velocity. The field results are verified by comparing the radar-derived images of volumetric moisture content to neutron log derived values. When an appropriate sitespecific conversion from radar wave velocity to moisture content is applied, a root mean square (RMS) error of 2.0-3.1% volumetric moisture content exists between the two sets. Further comparison of the two different data sets along with analysis of plots of the ray density through each cell indicates that regions of high moisture content are better resolved than regions of low moisture and that most of the discrepancy between radarderived and neutron-derived moisture contents occurs in regions of high moisture content. Better spatial resolution can be provided if dense station spacing is used. However, the amount of extra time required to acquire the extra data may limit the usefulness of the method. Repeatability measurements made with five data sets demonstrate that the precision error of the data acquisition system employed averages about 0.54 ns, which translates to about a 0.5% error in moisture content estimation.

[1] A sequential, geostatistical inverse approach was developed for electrical resistivity tomography (ERT). Unlike most ERT inverse approaches, this new approach allows inclusion of our prior knowledge of general geological structures of an area and point electrical resistivity measurements to constrain the estimate of the electrical resistivity field. This approach also permits sequential inclusion of different data sets, mimicking the ERT data collection scheme commonly employed in the field survey. Furthermore, using the conditional variance concept, the inverse model quantifies uncertainty of the estimate caused by spatial variability and measurement errors. Using this approach, numerical experiments were conducted to demonstrate the effects of bedding orientation on ERT surveys and to show both the usefulness and uncertainty associated with the inverse approach for delineating the electrical resistivity distribution using down-hole ERT arrays. A statistical analysis was subsequently undertaken to explore the effects of spatial variability of the electrical resistivity-moisture relation on the interpretation of the change in water content in the vadose zone, using the change in electrical resistivity. Core samples were collected from a field site to investigate the spatial variability of the electrical resistivity-moisture relation. Numerical experiments were subsequently conducted to illustrate how the spatially varying relations affect the level of uncertainty in the interpretation of change of moisture content based on the estimated change in electrical resistivity. Other possible complications are also discussed.

A method is presented for modeling the wideband, frequency domain electromagnetic (EM) response of a three‐dimensional (3‐D) earth to dipole sources operating at frequencies where EM diffusion dominates the response (less than 100 kHz) up into the range where propagation dominates (greater than 10 MHz). The scheme employs the modified form of the vector Helmholtz equation for the scattered electric fields to model variations in electrical conductivity, dielectric permitivity and magnetic permeability. The use of the modified form of the Helmholtz equation allows for perfectly matched layer ( PML) absorbing boundary conditions to be employed through the use of complex grid stretching. Applying the finite difference operator to the modified Helmholtz equation produces a linear system of equations for which the matrix is sparse and complex symmetrical. The solution is obtained using either the biconjugate gradient (BICG) or quasi‐minimum residual (QMR) methods with preconditioning; in general we employ the QMR method with Jacobi scaling preconditioning due to stability. In order to simulate larger, more realistic models than has been previously possible, the scheme has been modified to run on massively parallel (MP) computer architectures. Execution on the 1840‐processor Intel Paragon has indicated a maximum model size of 280 × 260 × 200 cells with a maximum flop rate of 14.7 Gflops. Three different geologic models are simulated to demonstrate the use of the code for frequencies ranging from 100 Hz to 30 MHz and for different source types and polarizations. The simulations show that the scheme is correctly able to model the air‐earth interface and the jump in the electric and magnetic fields normal to discontinuities. For frequencies greater than 10 MHz, complex grid stretching must be employed to incorporate absorbing boundaries while below this normal (real) grid stretching can be employed.

S U M M A R YAn iterative solution t o the non-linear 3-D electromagnetic inverse problem is obtained by successive linearized model updates using the method of conjugate gradients. Full wave equation modelling for controlled sources is employed to compute model sensitivities a n d predicted data in the frequency domain with an efficient 3-D finitedifference algorithm. Necessity dictates that the inverse be underdetermined, since realistic reconstructions require the solution for tens of thousands of parameters. In addition, large-scale 3-D forward modelling is required and this can easily involve the solution of over several million electric field unknowns per solve. A massively parallel computing platform has therefore been utilized t o obtain reasonable execution times, and results are given for the 1840-node Intel Paragon. The solution is demonstrated with a synthetic example with added Gaussian noise, where the data were produced from a n integral equation forward-modelling code, and is different from the finite difference code embedded in the inversion algorithm