SUMMARY: Full waveform inversion (FWI) of ground-penetrating radar (GPR) data is one of the key methods for reconstructing high-resolution near-surface images and understanding changes in the near-surface environment. However, FWI faces great challenges due to the ill-posed nature of the optimization problem and its high sensitivity to the quality of the initial model. In this paper, we propose the implicit full waveform inversion (IFWI) algorithm for GPR that addresses these limitations by using an implicit and continuous function built by deep neural networks to represent multiple subsurface parameters. In this way, IFWI can be thought of as training a physically constrained neural network representation, allowing it to exploit the frequency principle in deep learning optimization, that is first recovering the low-frequency components of the subsurface parameters and large-scale structures, and then their high-frequency and fine-grained information, which makes IFWI an automated multiscale inversion approach without manual frequency band selection. Numerical experiments are conducted to verify the effectiveness and performance of IFWI relative to FWI. The results demonstrate that IFWI can accurately reconstruct subsurface images and anomalies even with a suboptimal initial model, while FWI and multiscale FWI struggle to provide useful information due to being trapped in local minima. Consequently, IFWI exhibits less dependence on the initial model compared to FWI and multiscale FWI. To better understand the behaviour of IFWI and its sensitivity to neural network architecture, we comprehensively discuss the influences of these hyperparameters on IFWI, providing initial selection guidelines and practical guidance for adjustments.

Slow formations, characterized by shear-wave velocities lower than those of the borehole fluid, present significant challenges for shear-wave velocity estimation using monopole acoustic logging, primarily due to the absence of critically refracted shear waves. To address this limitation, a borehole full-waveform inversion framework is proposed in this paper, which employs low-frequency monopole excitation to exploit the sensitivity of Stoneley waves to shear velocity. The elastic wave equation is reformulated in cylindrical coordinates as a recurrent neural network structure within a deep learning framework, allowing automatic differentiation for efficient gradient computation without adjoint-state methods. Numerical experiments reveal that while high-frequency monopole data can accurately recover compressional-wave velocities, they fail to resolve shear-wave velocities due to weak Stoneley energy in the high-frequency data. In contrast, strong low-frequency Stoneley waves enable robust and reliable shear-wave inversion. An inversion workflow is further proposed, in which an initial shear-wave velocity model is derived by applying a velocity ratio to the inverted compressional-wave model and subsequently refined through inversion of low-frequency monopole data. The proposed approach yields high-accuracy shear velocity profiles in the near-wellbore region and remains effective under complex geological conditions, including small-scale anomalies and ultra-slow formations. These results highlight the critical role of Stoneley waves in monopole-based inversion and offer a practical solution for estimating the shear-wave velocities of slow and unconsolidated formations. Document Type: Original article Cited as: Xu, S., Liu, Y., Zou, Z., Liu, Y. Borehole full-waveform inversion of monopole logging in slow formations: Insights for shear-wave velocity profiling. Advances in Geo-Energy Research, 2025, 17(1): 82-90. https://doi.org/10.46690/ager.2025.07.07

Gas hydrate is seen as a kind of new energy resources, yet it may also be one of the main greenhouse gases as its dissociation may release methane into the atmosphere. Furthermore, a severe hazard to offshore infrastructures may also be introduced by extensive gas hydrate dissociation associated with the stability of the geological structures after gas production. Therefore, it is essential to investigate the gas hydrate as well as its environmental impacts before drilling and extracting it. The geophysical seismic reflection data is usually used for exploring the gas hydrate. The gas hydrate can be effectively identified by the bottom simulating reflectors (BSRs) on seismic reflection data. However, the BSR is only for identifying the bottom boundary and it is difficult to estimate its space distribution and saturation within the hydrate stability zone. The marine controlled-source electromagnetic (CSEM) data is suitable for detecting the gas hydrate as the resistivity of the seafloor increases significantly in the presence of gas hydrate or free gas. In this study, a weighted differential-field method is applied to improve the detectivity for identifying the gas hydrate. Numerical tests show that the difference of the EM fields can effectively suppress the airwaves in shallow waters. Therefore, the detectivity given by the field ratio between the models with and without the gas hydrate target is enhanced.

With global warming, Arctic sea ice, as one of the important factors regulating climate, has put forward new requirements for research. At present, the ground penetrating radar (GPR) is a powerful tool to obtain the structure of Arctic sea ice. Traditional offset imaging techniques no longer meet research requirements, and the two-parameter full waveform inversion (FWI) method has received widespread attention. To solve the high nonlinearity and ill-posed problem of FWI, the L-BFGS optimization algorithm and Wolfe criterion of inexact line search were used to update the model. The parameter scale factor, multiscale inversion strategy, and total variation (TV) regularization were introduced to optimize the inversion results. Finally, the inversion of anomalous bodies with different scales and different physical parameters is carried out, which verifies the reliability of the proposed method for dual-parameter imaging of Arctic sea ice and provides a powerful tool for the study of Arctic sea ice.

The marine controlled-source electromagnetic (CSEM) method offers a range of applications in marine geophysics, including mapping hydrocarbon reservoirs, studying seafloor geology, identifying potential mineral deposits, and investigating hydrate occurrences. Nevertheless, the interpretation of marine CSEM data becomes challenging when complex anisotropy is present. Therefore, we extend the scope of numerical simulations of 3-D marine CSEM responses from a specific case of dipping anisotropy to general anisiotropy based on a goal-oriented adaptive finite element (FE) method. We verify the upgraded argorithm using a 1-D anisotropic layered model with quasi-analytical solutions, and then apply the algorithm to compute marine CSEM responses over a set of 3-D anisotropic models. The behavior of the responses is explained by visualizing the flow of electromagnetic energy within the anisotropic conductivity structures using the Poynting vector, which helps explain how the energy propagates and interacts with the anisotropic media, providing insights into the observed response patterns. The numerical results clearly demonstrate that both the dipping and azimuthal anisotropy in the sediment have considerable impacts on marine CSEM responses, but their effects follow different patterns and highly dependent on the orientation of the electric dipole source utilized. However, the influence of the anisotropic reservoir can hardly be observed. This study emphasizes the significance of incorporating anisotropy into the interpretation of marine CSEM surveys. It contributes to the understanding of the impacts of general anisotropy on marine CSEM responses and provides a valuable approach for accurately modeling and interpreting marine CSEM data in the presence of anisotropic conductivity.

The impact of well deviation on wavefields in deviated boreholes within vertical transversely isotropic formations was investigated through numerical simulations. A 3D finite-difference model incorporated a cubic block containing a borehole, with a Kelly source used as the acoustic monopole source. Findings indicated that at zero well deviation, the wavefield exhibited isotropic characteristics, transitioning to anisotropic behavior as well deviation increases, characterized by a reduction in P-wave velocities along the x-axis while remaining constant along the y-axis. Analysis using slowness-time-coherence analysis revealed that P-wave dispersion decreased with increasing well deviation. Additionally, simulation results for Stoneley waves showed significant variations in amplitude and propagation velocity across different well deviation angles, with the most substantial dispersion observed in vertical wells and minimal dispersion observed in horizontal wells. The alignment of simulated results with field data supported the reliability, indicating that as well deviation increased, P-wave arrival times advanced, and amplitudes decreased, and Stoneley wave characteristics aligned with the simulated outcomes. These findings have substantial implications for optimizing logging and completion strategies, thereby enhancing the efficiency of exploration and development efforts in unconventional oil and gas reservoirs.