Airborne electromagnetic (AEM) methods are highly efficient tools for assessing the Earth's conductivity structures in a large area at low cost. However, the configuration of AEM measurements, which typically have widely distributed transmitter-receiver pairs, makes the rigorous modelling and interpretation extremely time-consuming in 3-D. Excessive overcomputing can occur when working on a large mesh covering the entire survey area and inverting all soundings in the data set. We propose two improvements. The first is to use a locally optimized mesh for each AEM sounding for the forward modelling and calculation of sensitivity. This dedicated local mesh is small with fine cells near the sounding location and coarse cells far away in accordance with EM diffusion and the geometric decay of the signals. Once the forward problem is solved on the local meshes, the sensitivity for the inversion on the global mesh is available through quick interpolation. Using local meshes for AEM forward modelling avoids unnecessary computing on fine cells on a global mesh that are far away from the sounding location. Since local meshes are highly independent, the forward modelling can be efficiently parallelized over an array of processors. The second improvement is random and dynamic down-sampling of the soundings. Each inversion iteration only uses a random subset of the soundings, and the subset is reselected for every iteration. The number of soundings in the random subset, determined by an adaptive algorithm, is tied to the degree of model regularization. This minimizes the overcomputing caused by working with redundant soundings. Our methods are compared against conventional methods and tested with a synthetic example. We also invert a field data set that was previously considered to be too large to be practically inverted in 3-D. These examples show that our methodology can dramatically reduce the processing time of 3-D inversion to a practical level without losing resolution. Any existing modelling technique can be included into our framework of mesh decoupling and adaptive sampling to accelerate large-scale 3-D EM inversions.

Steel-cased wells used as long electrodes (LEs) in a surface-electric or electromagnetic survey can enhance anomalous signals from deep hydraulic fracturing zones filled by injected fluid. Although recent research has been published on the algorithms designed for the simulation of the effect of casings, feasibility studies on resolving the small-scale fracturing fluid flow from surface data are lacking. We have carried out a detectability and recoverability study for a top-casing electric source configuration. Applying a fast 3D DC modeling code using the concept of the equivalent resistor network, the detectability study shows that favorable conditions for detecting the fluid flow direction include multiple electrically coupled wells and different electric conductivities above and below the shale layer. The observed behavior is then modeled through a circuit analog. For the purpose of fracturing fluid imaging, the model for recovery is simplified as a distribution of full fluid saturation on a 2D fracture plane. A deep-learning (DL) framework is adopted to solve the imaging problem, which is difficult for conventional regularized inversions. Our DL implementation uses a supervised deep fully convolutional network to learn the relationship between data patterns on the surface and the model of fracturing fluid distribution, encoded in a large number of synthetic data-model pairs. Once trained, the neural network can make a real-time and pixel-wise prediction of the fluid distribution. The robustness of our DL approach is tested in the presence of ambient noise and inaccuracies in casing conductivity. A reasonably consistent prediction performance has been observed. Our numerical feasibility study results demonstrate that electric surveys using steel casings as LEs have great potentials in real-time monitoring of fracturing fluid flow.

Numerical modeling and inversion of electromagnetic (EM) data is a computationally intensive task. To achieve efficiency, we have developed algorithms that were constructed from a smallest practical computational unit. This “atomic” building block, which yields the solution of Maxwell’s equations for a single time or frequency datum due to an infinitesimal current or magnetic dipole, is a self-contained EM problem that can be solved independently and inexpensively on a single core of CPU. Any EM data set can be composed from these units through assembling or superposition. This approach takes advantage of the rapidly expanding capability of multiprocessor computation. Our decomposition has allowed us to handle the computational complexity that arises because of the physical size of the survey, the large number of transmitters, and the large range of time or frequency in a data set; we did this by modeling every datum separately on customized local meshes and local time-stepping schemes. The counterpart to efficiency with atomic decomposition was that the number of independent subproblems could become very large. We have realized that not all of the data need to be considered at all stages of the inversion. Rather, the data can be significantly downsampled at late times or low frequencies and at the early stages of inversion when only long-wavelength signals are sought. We have therefore developed a random data subsampling approach, in conjunction with cross-validation, that selects data in accordance to the spatial scales of the EM induction and the degree of regularization. Alternatively, for many EM surveys, the atomic units can be combined into larger subproblems, thus reducing the number of subproblems needed. These trade-offs were explored for airborne and ground large-loop systems with specific survey configurations being considered. Our synthetic and field examples showed that the proposed framework can produce 3D inversion results in uncompromised quality in a more scalable manner.

Exploration Geophysics is published by CSIRO PUBLISHING on behalf of the Australian Society of Exploration Geophysicists. Papers published report the results of significant case histories and relevant original research in geophysics, with emphasis on the Australian and similar environments.

Surface transient electromagnetic (TEM) data with large transmitter loops for deep mineral exploration are often complicated by the non-trivial coupling between extended sources and arbitrarily oriented geological targets. This case study reports a TEM field data set acquired across terranes with strong lateral inhomogeneity, which is responsible for the high inconsistency in TEM data patterns along the survey line, as well as for the negative TEM transients (sign reversal) at some near-central loop stations. 3D forward modeling and inversion, as maturing tools in recent years, offer unique opportunities to extract as much geological information from such data as possible. 3D forward simulations of representative synthetic models found that the phenomenon of sign reversal at some TEM stations is associated with compact conductors enclosed by the transmitter loop and receivers that are in the loop, but off the conductor–a situation that is common in large-loop TEM and can only be explained by 3D models. However, 3D inversion of the field data with a uniform subspace as the initial and reference model fails to converge, another point of evidence that 3D inversions of large-loop TEM data are more likely to be subject to stability issues. Our solution is to warm-start the inversion with the representative model in the forward simulation experiments as the initial model, so the ill-posed 3D inversion can escape from local minima. Finally, the vertical contact structure in our 3D-inversion model is verified by a resistivity cross section of the CSAMT method. Our case study demonstrates the demand and capability of 3D electromagnetic modeling and inversion for high-resolution deep mineral exploration. It also provides an easy-to-follow template for carrying out 3D interpretation for complex geology in practice.

Electrically conductive fluid injected during the operation of hydraulic fracturing can be mapped by anomalous electromagnetic (EM) fields under the excitation of controlled EM sources on the surface. Steel casings, existing in the area of fracturing operation and sources of strong EM responses, need to be considered during survey design and data interpretation. We develop a novel numerical approach that efficiently simulates the effect of steel casings by assigning lumped conductive properties to mesh edges, so finely discretized cells for casings are avoided. In our numerical study, a multistage fracturing along a horizontal well is considered, and the operation is monitored by two survey configurations: long-offset excitation and near-well excitation. Time-lapse analysis shows that the steel casings always have nonnegligible effect in EM field data, even for the long-offset excitation with a distant EM source; the near-well excitation, compared with other conventional configurations, has stronger EM responses and can maintain a high level of sensitivity throughout the entire fracturing procedure. Our results help the design of a controlled source EM survey for a more effective fracturing monitoring campaign.