Abstract Testing soil samples for geoengineering and geotechnical applications requires the measurement of various properties. In this study, we propose a simple yet robust method to determine the dielectric constant profile of soil cores. The proposed method measures a set of transillumination signals using an ultra-wideband radar, which records the travel time of microwaves penetrating through cylindrical soil core samples with various path lengths. The shape of the arrival time curve is evaluated using a modified version of the velocity analysis for common mid-point data to obtain the dielectric constant that best describes the shape of the curve. This method can be used to obtain depth profiles of, for example, soils varying in type and water content. Laboratory experiments were conducted with simulated soil core samples with a diameter of 75 mm with various types of soils and varying water content, using an implementation of the proposed measurement system operating in the 0.5–2.8 GHz frequency range. The results show that the proposed approach obtains reasonable values for the dielectric constants of the samples, which are consistent with those predicted using mixing models. Thus, the results demonstrate that the system can perform the automated measurement of the dielectric constant profile of soil cores.
Abstract Reducing uncertainty in thin-layer geological interpretation requires detailed characterization of the spatial distribution of thin layers. Time-frequency (TF) analysis (TFA) reveals the energy distribution and variation of seismic signals across time and frequency, facilitating the extraction of thin-layer information. However, conventional TFA methods suffer from interlayer interference, making accurate spatial characterization of thin-layer interfaces challenging. Therefore, a method termed NMF-TSTFGWT is proposed, which incorporates non-negative matrix factorization (NMF) into the time-reassigned synchrosqueezing tunable-factor Gabor wavelet transform (TSTFGWT) to enhance the TF representation (TFR). Within TSTFGWT, the tunable-factor Gabor wavelet provides locally adaptive temporal resolution. The resulting tunable-factor Gabor wavelet coefficients are then reassigned along the temporal dimension using synchrosqueezing, thereby enhancing temporal resolution. Furthermore, the magnitude of the TSTFGWT coefficients is computed to construct the TFR, and NMF is employed to reduce redundancy in the spectral features. This low-rank decomposition captures the TFR's dominant features, suppresses spectral interference, and improves TF resolution. Experiments on synthetic and field seismic data show that the proposed NMF-TSTFGWT improves energy concentration in the TFR and yields more accurate thin-layer characterization. Moreover, derived thin-layer interfaces are consistent with well-log interpretations, effectively delineating thin-layer interfaces and improving the reliability of geological interpretations.
Abstract Rock-physics relations introduce strong nonlinearities into the mapping between reservoir properties and prestack seismic records. Conventional elastic-property-based twostep inversion workflows usually estimate elastic parameters first and then transform them into petrophysical parameters through rock-physics models. Such a sequential strategy is inherently susceptible to error propagation, especially when elastic inversion and rock-physics prediction are coupled through nonlinear relations. Although linearized direct inversion can partly reduce the error propagation associated with the two-step workflow, its accuracy remains limited by local linearizations of both seismic reflectivity and rock-physics relations, especially at large incidence angles or in strongly heterogeneous reservoirs. Moreover, explicit derivation of the resulting composite Jacobian is model-dependent and cumbersome. To address these limitations, this study develops a nonlinear direct petrophysical inversion framework based on automatic differentiation. The proposed method integrates the Zoeppritz equation with nonlinear rock-physics relations to construct a unified differentiable forward operator, which directly maps porosity, water saturation, and shale volume fraction to prestack seismic records. Automatic differentiation is used to calculate the gradients of the nested nonlinear forward operator, avoiding model-specific analytical Jacobian derivations and improving the flexibility of the inversion implementation. To improve the stability of multiparameter inversion, structure-guided regularization is further introduced to promote lateral continuity along geological structures while preserving sharp petrophysical contrasts across stratigraphic boundaries. Synthetic examples and a field application show that the proposed method improves the stability and geological consistency of petrophysical inversion, especially in terms of lateral continuity and preservation of major lithological and fluid-related boundaries.
Abstract Horizontal transverse isotropic (HTI) media, the simplest model of azimuthal anisotropy, are characterized by a parallel arrangement of vertical fractures. HTI media have been extensively studied in the exploration of hydrocarbon reservoirs with vertical or high-angle fractures. However, there are often multiple solutions for fracture prediction based on HTI, owing to insufficient prior fracture information and differences in parameter contributions. Accordingly, we have developed a fast geometric attribute-constrained prestack seismic inversion (FGCI) method based on two geometric attributes: dip and curvature. First, based on numerous variables causally related to fracture density, including fracture-inferring material properties, curvature attributes, and their combined parameters, a curvature-constrained kernel function is constructed using a linear regression model. This function accounts for two types of fractures, structural and background fractures, providing reasonable prior information for fracture prediction and exhibiting a high correlation with fracture density. Second, the expression for the geometric attribute-constrained operators is derived by embedding the curvature-constrained kernel function into the multitrace inversion framework, thereby improving the universality of the curvature for quantitatively characterizing the fracture density. Finally, a computationally well-behaved objective function was established and written in Lagrangian form. Considering multiple constraints, adaptive penalty factors have been developed to balance the residuals of the primary and dual problems, thereby ensuring computational efficiency and stability. Numerical experiments and field data applications demonstrate that this method exhibits strong stability, spatial fidelity, and resolution. Thus, FCGI can provide a stable, efficient, practical, and comprehensive solution for predicting fractures in complex structures.
Abstract Peatlands are crucial in the global carbon and nutrient cycles for water regulation and biodiversity, highlighting their importance, especially during times of global change. Grasping the spatial extent, depth, and internal structure of peatlands is essential for effective restoration and sustainable land use. Geophysical methods provide valuable information on these characteristics at various scales, with varying spatial resolutions and accuracies. This study integrates multiple ground-penetrating radar (GPR) techniques and surface nuclear magnetic resonance (SNMR) to characterize a drained peatland in the Gnarrenburg area of Germany. Constant-offset GPR mapping was used to image peat geometry and internal stratigraphy, while common-midpoint and guided-wave soundings provided velocity–depth models for estimating volumetric water content. SNMR sounding data were inverted to obtain relaxation-time distributions and water-content profiles, and GPR-derived reflectors were incorporated as structural constraints to improve inversion sharpness of internal layering of the peat. Soil cores and ring samples served as ground truth. Our results demonstrate that guided-wave GPR yields precise interval-velocity and water-content profiles, resolving small-scale variations and transitions at the peat–mineral interface. Constrained SNMR inversions successfully identify distinct relaxation-time signatures associated with varying peat decomposition states, particularly distinguishing less and highly decomposed layers. Thus, the combination of GPR and SNMR provides a comprehensive, minimally invasive framework for assessing peatland stratigraphy, water content distribution, and peat degradation. Given their relevance in global change, these encouraging outcomes present an opportunity to observe changes over time, making them especially suitable for costeffective monitoring purposes due to their minimally invasive characteristics. Ultimately, these methods serve as valuable tools for planning and guiding peatland restoration activities.
