Geophysical Prospecting最新文献

The Athabasca Basin (Saskatchewan, Canada) is a world-class uranium mining province hosting high-grade high-tonnage deposits. Electromagnetic data and direct current resistivity data are essential tools to detect deep geoelectric structures associated with mineralization. Both methods are sensitive to electrical resistivity but highlight different structures. On the one side, electromagnetic methods reveal deeply buried, highly conductive graphitic structures. On the other side, direct current resistivity methods reveal milder contrasts of resistivity at shallower depths. We are here exploring the benefits that can be expected from two-dimensional joint inversion of electromagnetic and direct current resistivity data for the exploration of unconformity-related uranium deposits of the Athabasca Basin. Our methodology is recovering a single resistivity model to fit both datasets. We used a trust-region globalization approach to regularize the local minimization sub-problems, thus avoiding the task of regularization parameter tuning. Several tests are first conducted on synthetic models. These tests show that stand-alone electromagnetic inversions are able to recover the position of conductive plates, but their geometry remains uncertain. On stand-alone direct current resistivity inversions, the layered background is recovered, as well as smeared anomalies of resistivity associated with graphitic conductors. Whenever a conductive halo overlies a conductive plate, the wide anomaly associated with the plate appears more conductive and slightly shallower but the conductive plate and the halo cannot be distinguished. In the presence of two closely spaced conductors, the conductive anomaly appears more conductive and wider, so that they cannot be distinguished. On stand-alone electromagnetic inversions, however, their separation is clear, but electromagnetic measurements are blind to alteration halos. Joint inversions give the most reliable models. Both the resistive background and the conductive plates are recovered. A better constrained background allows to recover more contrasted plates. Synthetic tests allowed us to confirm the potential to recover the footprint of a hectometric-scale conductor overlying a plate using joint inversion, where both stand-alone inversions failed. Following these synthetic tests, we present an application of our methodology to a dataset from the Waterbury–Cigar Lake area. Joint inversion allows to recover a geoelectric model reconciling both datasets. The model shows conductors better constrained below the depth of unconformity, allowing for interpretations of resistivity variations above them.

Full-waveform inversion (FWI) of seismic data is a powerful method for estimating high-resolution models of the subsurface. An accurate initial model and low-frequency data are necessary to avoid cycle skipping and perform a successful FWI. In the absence of this information, FWI is likely to fail due to convergence in local misfit minima. With the recent advancements in artificial intelligence, studies have shown that absent low-frequency data can be extrapolated using deep learning (DL). These studies have been mostly focused on surface seismic data whose frequency content is different from cross-well data. In this study, we assess the use of DL for low-frequency extrapolation for a cross-well survey that was done at the Aquistore $�{\mathrm{CO}}_2$ storage site in Saskatchewan. This assessment includes both numerical and field data examples. We extrapolate the low frequencies to increase the bandwidth of the acquired data at the Aquistore site and perform FWI. We evaluate the efficiency of this method by comparing the results with obtained velocity models from the conventional multiscale FWI. Our results for the Aquistore data show that the proposed strategy leads to an accuracy improvement of 39% and 20% in the model and data domains, respectively.

Conventional reflection waveform inversion solves a two-parameter seismic inverse problem alternately for subsurface reflectivity and acoustic background velocity as the model parameters. It seeks to reconstruct a low-wavenumber velocity model of the subsurface from pure reflection data cyclically, through alternating migration and tomography loops, such that the remodelled data fits the observed data. Low-resolution seismic images with unpreserved amplitudes, full-wave inconsistency in the short-offset data and cycle skipping in the long-offset are perceived as the main reasons for suboptimal tomographic updates and slow convergence in conventional reflection waveform inversion. In the context of one-way reflection waveform inversion, this paper addresses the listed limitations through four main components. First, it augments one-way reflection waveform inversion with a computationally affordable preconditioned least-squares wave equation migration algorithm to ensure high-resolution reflectors with preserved amplitudes. Second, the paper verifies how well the full-wave consistency condition in the short-offset data is satisfied in one-way reflection waveform inversion and suggests muting inconsistent short-offset residual waveforms in the tomography loop to attenuate their adverse imprint. Third, the paper suggests extending the migration offset beyond short offsets to improve both the illumination and the signal-to-noise ratio of the reflectors. Fourth, the paper presents a data-selection algorithm to exclude the damaging effect of the cycle-skipped long-offset data in the tomography loop. The effectiveness of the proposed one-way reflection waveform inversion algorithm is finally validated through three numerical examples, demonstrating its capability to recover high-fidelity tomograms.

Accurate and efficient modelling of subsurface electrical properties is critical for a wide range of applications, including mineral exploration, environmental studies and hydrogeological investigations. Traditional numerical approaches often use low-order discretization and impose artificial boundary conditions to approximate the unbounded spatial domain. These approximations can lead to inaccuracies and computational inefficiency, particularly in geologically complex environments. In this study, we present a spectral-infinite-element method (SIEM) for forward modelling of electrical resistivity and induced polarization. The approach couples high-order spectral elements within the finite domain with a single outer layer of mapped infinite elements, enabling precise representation of far-field boundary conditions. To achieve optimal numerical performance, we employ two distinct quadrature schemes: Gauss–Legendre–Lobatto quadrature for the spectral elements and Gauss–Radau quadrature for the infinite elements. We first verify the accuracy of our method by comparing the computed electric potential from a buried charged block with direct numerical integration. We conducted a convergence study by refining the mesh and increasing the order of the interpolation polynomials. To further evaluate the robustness of SIEM, we benchmark its results for a layered earth model against an analytical solution and an open-source Python-based geophysical modelling library, SimPEG. The comparisons demonstrate the accuracy, convergence and efficiency of SIEM. Finally, we apply SIEM to a complex heterogeneous conductivity model incorporating topography, generating apparent resistivity and chargeability pseudo-sections to illustrate its practical applicability under realistic survey conditions.