Geophysical Prospecting - Latest issue

A medium with homogeneous anisotropic layers that are thin compared with the elastic wavelength can be replaced with an equivalent anisotropic medium by the process of Backus averaging. The equivalent medium for isotropic and transversely isotropic layers (where the axis of symmetry is normal to the layering) is transversely isotropic. Transversely isotropic media can be described by the symmetry axis, two axial velocities and three dimensionless parameters. In this paper, we derive simple expressions for these dimensionless parameters in terms of differences of elastic parameters between the layers. We investigate the signs of the dimensionless parameters and conditions for zero parameters in layering with two isotropic media or an isotropic and a transversely isotropic medium.

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.

This research presents an innovative framework for lithology detection that combines domain adaptation, an Actor–Critic reinforcement learning (RL) architecture and Transformer‐based sequence modelling to enhance log interpretation reliability in complex depositional environments. The study first reviews conventional petrophysical characterization methods using wireline measurements, noting their limitations in dealing with varied lithofacies distributions and non‐stationary formation properties. Subsequently, it emphasizes the superior capabilities of neural networks, particularly the Transformer architecture, in analysing temporal measurement sequences. The multi‐head attention mechanism in Transformers effectively models contextual relationships within depth‐dependent logging signals, which is vital for stratigraphic interpretation. The proposed framework incorporates the Actor–Critic reinforcement paradigm, where the policy network (Actor) generates lithofacies predictions, and the value network (Critic) evaluates prediction quality. This dual‐network setup promotes iterative policy refinement through feedback, enhancing classification consistency and computational efficiency. Moreover, recognizing the potential for domain shifts in logging campaigns, the framework includes parameter transfer mechanisms to facilitate knowledge distillation from source to target domains. This ability to adapt across projects significantly boosts model robustness and deployment feasibility in diverse reservoirs. Experimental validation on multiple well‐log datasets shows that the combined Transformer architecture, RL, and transfer strategies outperform traditional machine learning and standalone deep learning models. Quantitative results reveal improvements in prediction accuracy, cross‐well generalizability and domain adaptation efficiency in novel geological environments.

The objective of this research is to estimate the elastic properties of the CO2 plume in the Sleipner field and perform a comparative analysis of model‐based inversion (MBI) and sparse layer reflectivity (SLR) inversion techniques. MBI is relatively old method, whereas SLR is relatively new method for seismic inversion. Model‐based seismic inversion is a well‐established deterministic inversion technique that iteratively minimizes the misfit between observed and modelled seismic data. In contrast, SLR inversion is designed to identify and analyse the reflectivity of thin subsurface layers by emphasizing sparsity in the reflectivity sequence. This study utilizes a set of time‐lapse seismic angle stack data from the Sleipner field, comprising a 1994 pre‐injection baseline and a 1999 post‐injection monitor survey, following the injection of 2.35 million tons of CO2. These angle stacks were used to generate P‐wave and S‐wave reflectivity using the two‐term Fatti amplitude versus offset (AVO) equation, which was then further utilized in the inversion process to estimate the elastic parameters. Acoustic and shear impedance (SI) were derived using MBI and SLR to evaluate their strengths, limitations, computational efficiency and adaptability to geological changes. In the CO2‐injected zone, acoustic impedance values were observed between 2000 and 2400 m/s g/cm³, whereas SI values ranged from 100 to 400 m/s g/cm³. Our findings suggest that overall, MBI produces sharper and more reliable imaging across the entire seismic section. For P‐impedance, MBI yielded correlation values of 0.980 with an error of 0.137 in 1994 and 0.989 with an error of 0.141 in 1999 datasets, whereas SLR showed higher correlation at the well location 0.997 with an error of 0.073 in 1994 and 0.998 with an error of 0.061 in 1999. For S‐impedance, MBI achieved correlation values of 0.860 with an error of 0.650 in 1994 and 0.974 with an error of 0.265 in 1999 datasets. In comparison, SLR produced a correlation of 0.995 with an error of 0.072 in 1994 and 0.951 with an error of 0.370 in 1999 datasets at the well location. However, similar to the P‐impedance case, whereas SLR performed well at the well location, its application to the full seismic volume resulted in reduced performance, characterized by noisier results and longer processing time. A comparative evaluation of MBI and SLR indicates that MBI offers greater efficiency, simpler implementation and faster computational performance. As a result, the impedance outputs obtained from MBI were subsequently converted into density, P‐wave velocity and S‐wave velocity using empirical relationships derived from well log data. In the seismic volumes, a significant change in the reservoir's elastic properties was observed in the CO2‐saturated zone, compared to the Utsira Formation, which serves as the reservoir into which CO2 has been injected. Density decreased from 1.75 to 1.35 g/cm³ (∼23%), P‐wave velocity from 2000 to 1820 m/s (∼9%) and S‐wave velocity from 1150 to 638 m/s (∼45%). These changes reflect the effects of CO2 replacing brine in the pore space, leading to a reduction in bulk density and stiffness and indicating overall reservoir softening due to gas injection. Integrating these inversion methods with multi‐parameter elastic estimation enables effective CO2 plume monitoring and reservoir characterization, highlighting the role of seismic inversion in detecting fluid‐induced changes and supporting improved monitoring strategies in carbon capture and storage (CCS) operations.

Least‐squares migration (LSM) is one of the most accurate imaging methods in seismic exploration. In recent years, image‐domain LSM (ID‐LSM) based on the approximated Hessian matrix has received widespread attention and development. How to effectively represent the Hessian matrix and implement the ID‐LSM efficiently and stably remains challenging. This study proposes an efficient computation method for the Hessian matrix and develops a data‐driven high‐resolution imaging scheme to promote the application of LSM. Specifically, we first introduce the analytical expression of the Hessian matrix within the framework of inversion imaging, leveraging the sparsity of the Hessian matrix and using point‐spread functions (PSFs) to approximate it. Then, considering the nonlinear characteristics of image‐domain PSFs deconvolution, we employ deep learning to construct a data‐driven imaging correction network. Finally, we incorporate features from the target data into the network, achieving efficient and faithful imaging of subsurface reflection coefficients. Through the computation cost analysis of PSFs construction, the developed method significantly reduces the computational costs, achieving only one‐fourteenth of the modelling–migration method based on the wave equation. The synthetic and field data examples demonstrate the effectiveness of the proposed data‐driven imaging scheme in both the spatial and wavenumber domains.

Raypath tracing is a commonly used technique in geophysics, employed to simulate and analyse seismic wave propagation paths from source to receiver in complex media. In isotropic media, raypaths can be obtained by tracing from the receiver point along directions perpendicular to the wavefront towards the source point, based on the Fermat principle, because in isotropic media, the ray direction aligns with the ray gradient direction. In an anisotropic medium, the ray direction generally differs from the ray gradient direction, rendering the conventional tracing method inaccurate. Solving raypaths using Hamilton's canonical equations is a powerful method. However, in anisotropic media, the complex dependence of wave velocity on the propagation direction complicates the Hamiltonian function, significantly increasing computational complexity. To address this problem, we have derived a scheme based on the relationship between the group velocity vector and the slowness vector in anisotropic media. Firstly, the slowness vector is derived from the traveltime obtained through the eikonal equation, followed by the computation of the group velocity vector. Then, the raypath is determined by tracing back from the receiver point using the group velocity components to the source point. The efficiency and accuracy of our approach are validated through three numerical experiments.

Analytical formulae for P‐ and SV‐wave normalized attenuation coefficients play a key role in attenuation anisotropy parameter estimation in dissipative transversely isotropic media with a vertical symmetry axis. We revisit approximate formulae for the P‐ and SV‐wave normalized attenuation coefficients. We use perturbation theory to derive accurate formulae for P‐ and SV‐waves in such media. The proposed formulae can reduce to simple expressions for attenuative elliptical anisotropic media and isotropic media. From the perturbation‐based formulae, we obtain the linearized formulae, the second‐order formulae and the fractional formulae. We use numerical examples to test the accuracy of the proposed formulae and implement the perturbation‐based formulae and the second‐order formulae in an inversion scheme to estimate the attenuation anisotropy parameters. From the numerical examples, we analyse the validity of the approximate formulae in computing the normalized attenuation coefficients and attenuation anisotropy parameter estimation.

Prestack depth migration requires a velocity model that extends from the surface through shallow to deep layers. Generally, the shallow velocity model is built using first‐arrival wave, under which the velocity model is built from reflected wave. These two models are integrated to form a continuous velocity model spanning from the surface to deep layers. Conventional shallow velocity model building methods are restricted by non‐uniqueness of inversion, limited inversion depth and poorly defined reflection interface, which hinders their integration with deeper reflection‐derived velocity models. Errors in the shallow velocity model can compromise the imaging quality of mid‐deep. In this article, we propose a 3D shallow velocity model building method using the joint inversion of first‐arrival and reflection traveltimes constrained by well data. This approach enables simultaneous inversion of velocity and reflection interfaces, thereby facilitating the integration of shallow and mid‐deep velocity models. Combining the first‐arrival and reflected waves enhances ray coverage angles and folds. By constraining the inversion for velocity and reflection interface with logging velocity and drilling depth, respectively, the method effectively mitigates the non‐uniqueness of inversion, thereby improving the accuracy of shallow velocity model. The inverted reflection interface can also be used for integration of shallow velocity model and its underlying velocity model. The theoretical model test proves the feasibility of the method, and the field data application verifies the effectiveness of the method.

We present a robust method for inverting magnetic data to estimate the three‐dimensional (3D) shape of a single targeted source in the presence of non‐targeted sources, without requiring prior filtering of interfering signals. Assuming knowledge of the total magnetization direction of the target, our method retrieves its total magnetization intensity, position and shape. The target is approximated by a set of vertically juxtaposed prisms with the same magnetization vector and thickness. Each prism's horizontal section is defined by a polygon with equally spaced vertices from to . The parameters to be estimated during inversion include the positions of the vertices, the horizontal location of each prism and the prism's thickness. The method uses a regularized non‐linear inversion with a data‐misfit function defined by L1‐norm data residuals (L1‐misfit solution). Tests on synthetic data demonstrate that the L1‐misfit solution outperforms the L2‐misfit solution in retrieving the 3D shape of the targeted source in the presence of non‐targeted sources. In the absence of interfering signals, both solutions yield similar results. Real data applications to the Anitápolis and Diorama alkaline complexes in Brazil suggest that both complexes are controlled by faults, consistent with published geological information.

Seismic inversion plays a critical role in populating subsurface properties into geomodel; however, its effectiveness is often constrained by the resolution limits of seismic data. This study evaluates the effectiveness of stochastic versus deterministic acoustic‐impedance inversion for estimating total porosity (Φtotal), effective porosity (Φe) and permeability (K) in the complex reefal carbonates of the Soğucak formation, Deveçatağı oil field—northwestern Thrace Basin, Türkiye—whose thickness ranges from 2 to 57 m. The wedge model indicates a tuning thickness of 60 m in Soğucak formation which limits the vertical resolution of the deterministic inversion that directly affects the geomodel resolution. To address this challenge, a stochastic inversion was performed to resolve beds below the tuning thickness, using a high‐resolution 1 millisecond (ms) vertical sampling grid and producing non‐unique, fine‐layered impedance realizations. Petrophysical relationships were established using 75 core plugs showing strong porosity–permeability trends that were cross‐validated with wireline logs. These relationships were applied to acoustic‐impedance volumes derived from both deterministic and stochastic inversion. Correlations at well locations used in the model are naturally higher due to constraints from acoustic‐impedance logs; therefore, we emphasized blind‐well correlations to assess predictive performance at locations without well control. Blind‐well tests at W‐2, W‐4, W‐6 and W‐11 demonstrate valid predictive capability, with stochastic inversion achieving correlations of 0.65–0.73 compared to 0.45–0.52 for deterministic inversion, effectively resolving sub‐tuning thickness beds and reliably predicting porosity and permeability. By overcoming the resolution limitations of deterministic inversion, stochastic inversion supported by robust petrophysical relationships—when applicable—provides a reliable and field‐proven tool that can be adapted to carbonate reservoirs in diverse geological settings worldwide.

Multicomponent seismic exploration technology comprehensively utilizes the dynamics and kinematics characteristics of seismic waves, which can reduce the non‐uniqueness of seismic imaging and predict hydrocarbon‐bearing reservoirs with high accuracy. However, multicomponent seismic data are often incomplete due to the constraints from field environment and acquisition costs. To address the problem of reconstructing multicomponent seismic data, conventional compressed sensing (CS)‐based algorithms reconstruct each component independently, failing to exploit implicit relationships between different components. By mapping different components to the real and imaginary parts of complex numbers, we can quantify their intrinsic correlations through the mathematical structure of complex numbers and preserve the cross‐information between different components. Therefore, under the framework of CS, we construct the objective function of joint reconstruction of two‐component seismic data and propose a joint reconstruction method of two‐component seismic data based on CS and complex Curvelet transform (CCT). First, we introduce complex numbers to establish the implicit relationship between different components by taking them as real and imaginary parts. The complex numbers constructed from different components are then subjected to the CCT as a whole. In this process, the joint sparsity of the two components is used as a priori information for data reconstruction. Finally, the proposed reconstruction model is solved using an improved fast projection onto convex sets. Experiments with synthetic and field data demonstrate that the proposed method effectively achieves the joint reconstruction of two‐component seismic data. Compared with reconstruction methods only using a single component of seismic data, the proposed method exhibits higher computational efficiency and accuracy without increasing data dimensions.

Acoustic emission (AE) is elastic waves generated spontaneously from the creation of micro‐cracks. AE waveforms share significant similarities with microseismic signals and serve as an effective tool for improving the understanding of fracture processes during hydraulic fracturing. AE events typically have small magnitude with low amplitude. To detect weak AE events, it is always necessary to set a larger gain control, but this increases the risk of large amplitude waveform being clipped beyond the saturation level of the A/D converter. Amplitude‐clipped AE events are usually considered unusable and must be excluded from the estimation of source properties such as focal mechanisms. We introduce an extension of compressed sensing methods to reconstruct the clipped waveform and further use them to perform the moment tensor inversions and decomposition. This method assumes that the AE events are band‐limited and the clipped segment of the waveform shares the same frequency content as the unclipped segment. Compared to conventional techniques, the proposed method can effectively reconstruct the clipped waveforms with clipping level less than 0.7, ensuring reliable moment tensor inversions and decomposition. The reconstruction method reduces the risk of confounding reasoning or misinterpretation caused by waveform distortion and provides a more reliable basis for the physical interpretation of AE properties.

The amplitude variation with offset (AVO) inversion method is crucial for predicting lithology and identifying fluids in hydrocarbon reservoirs. It is especially useful for evaluating shale oil or shale gas reservoirs. The accuracy of AVO inversion is critical to the quantitative interpretation of lithology and hydrocarbon‐bearing properties in reservoirs with vertical transverse isotropic (VTI) features. This work proposes a rock‐physics‐constrained anisotropic AVO inversion method to achieve stable density estimations in VTI media. This method establishes a new parameter set with density as an independent variable by transforming the conventional elastic parameter domain into a deviation parameter domain through rock‐physics‐constrained equations. Combined with the explicit form of the Rüger approximation, this approach not only eliminates correlations among conventional inversion parameters and mitigates the impact of anisotropy but also significantly improves the accuracy of density inversion. The feasibility and effectiveness of the proposed inversion method are demonstrated through the application of synthetic and field seismic data.

Deploying large datasets for training machine learning models often reveals more information about the target variable and helps to avoid overfitting. However, these advantages are associated with certain challenges, such as data noise and redundancy. In the present study on well log data consisting of a relatively large dataset (40 wells from the Cambay Basin), we deploy different classes of feature selection methods (filter‐based methods, wrapper‐based methods and embedded methods) to obtain the optimal feature set aimed at accurate prediction of sonic logs. Additionally, we utilize methods such as the boxplot and histogram analysis to remove outliers present in the dataset. Subsequently, we use XGBoost as our machine learning model, with fivefold cross‐validation and a 70:30 split. We then proceed to predict the sonic log data in a blind well. We establish that the maximum relevance minimum redundancy method shows the best results with an R‐squared value of 63% when we select three out of six features – depth, neutron porosity and bulk density. Significance of the results was demonstrated using statistical tests of significance, namely one‐way analysis of variance and Tukey's honestly significant difference test. The selection of these features is further validated by established geophysical principles in the form of empirical relationships.

Urban underground tunnelling faces challenges from small‐scale unfavourable geological bodies such as boulders and karst caves, the diameters of which are less than 1 m mostly. To address this issue, the tunnel‐array acoustic wave prospecting technique has been proposed. It utilizes piezoelectric transducers to excite acoustic waves with a central frequency of 4000 Hz, enabling the detection of small‐scale unfavourable geological bodies ahead of tunnel. However, due to the excavation by the shield cutterhead, the cracks and fissures in the rock mass near the cutterhead will significantly develop, forming a disturbed zone with high inhomogeneity. The existence of the disturbed zone will cause severe multiple scattering, which induces artefacts in the imaging results and reduces the accuracy of the advanced prospecting results. In terms of above issues, we introduce the idea of multiple signal classification (MUSIC) algorithm into the reflection matrix method and propose a novel MUSIC algorithm–based reflection matrix method. The reflection matrix can achieve the imaging of reflectors through re‐projecting the acquired data into the media at excitation and reception using Green's function. But it cannot deal with the artefacts induced by multiple scattering. The idea of MUSIC algorithm is to calculate the correlation between Green's function and the singular vectors of the signal or noise subspace, which are obtained by singular value decomposition (SVD) of covariance matrix of the acquired data, achieving estimation of the reflectors. Referring to this idea, we further improved the reflection matrix using MUSIC algorithm. The reflection matrix method is applied first, and the reflection matrix is obtained. Then by SVD of covariance matrix of the reflection matrix, we obtain the signal vectors related to the imaging results of reflectors and noise vectors related to artefacts. The signal vectors are used to calculate the correlation with an imaging operator K, which is derived from the product of the conjugate of Green's function and itself. When the computing grid within reflectors, the results reach the local maximum; otherwise, it tends to 0. In this way, we mitigate the imaging artefacts introduced by the multiple scattering. Through synthetic experiment, we verified that the proposed method can effectively suppress the imaging noise and improve resolution of the imaging results compared to the reflection matrix method. Finally, the proposed method was applied on field data obtained in Zhanmatun Iron Mine and successfully predicted the interface of the opposite tunnel in the target area.