ABSTRACT High‐quality seismic data are crucial for interpreting subsurface structures. Due to economic costs, geological obstacles and other factors, the acquired seismic data are often sampled regularly or irregularly along spatial coordinates. Such incomplete data pose significant challenges to subsequent seismic data processing workflows, making seismic data reconstruction one of the most cost‐effective and critical techniques to address this issue in seismic exploration. While deep learning has yielded promising results in data reconstruction, supervised methods rely heavily on large volumes of labelled seismic data – a resource that is scarce in practical applications. To tackle this limitation, this study proposes a self‐supervised learning method for seismic data reconstruction, incorporating a global‐aware blind‐trace mask mapper and squeeze‐and‐excitation (SE) attention blocks. First, we introduce a global‐aware blind‐trace mask mapper, which mitigates the identity mapping problem in self‐supervised learning frameworks and leverages global information to enable efficient reconstruction. This mapper samples all traces at the blind‐trace positions on the interpolated volume and maps them to the same channel, allowing the loss function to optimize all blind traces simultaneously. Second, we adopt a nested U‐Net (UNet++) architecture integrated with SE attention blocks as the interpolation network, enhancing the model's capacity to capture key features. Third, we guide network training using a hybrid loss function combining mean absolute error and structural similarity index, which captures both fine‐grained details and global structural information to ensure reconstruction quality. Experiments on synthetic and field datasets demonstrate that our method outperforms existing approaches in interpolation accuracy.

ABSTRACT Full‐waveform inversion (FWI) is a standard velocity model‐building (VMB) technique in seismic exploration, yet conventional implementations struggle to deliver high‐resolution three‐dimensional (3D) velocity models due to strong dependencies on accurate initial models – frequently unavailable in field applications. While data‐driven deep learning solutions show potential, they face significant challenges including prohibitive training data requirements, computational burdens and limited generalization. To overcome these limitations, we propose a low‐rank adaptive U‐Net with attention gates (LAUNet): a novel foundation‐model‐inspired two‐dimensional (2D) U‐Net framework integrating enhanced low‐rank adaptation (LoRA) and attention gates, enabling parameter‐efficient fine‐tuning for 3D seismic VMB. The method processes 2D initial velocity slices and the corresponding reverse time migration slices extracted from 3D volumes, leveraging self‐attention to fuse multimodal features for enhanced VMB precision. This approach resolves dimensionality inconsistencies inherent to acquisition geometries while reducing computational costs compared to 3D FWI. Additionally, we introduce an adaptive LoRA fine‐tuning strategy that incorporates well‐log data. By leveraging pre‐trained representations and multimodal inputs, LAUNet achieves high fidelity in velocity prediction with minimal labelled data, demonstrating strong generalization across complex geological scenarios. Experimental validation on 3D Overthrust and SEAM models demonstrates LAUNet's superiority over traditional FWI. Numerical results confirm enhanced inversion accuracy in deep, complex geological structures alongside improved computational efficiency and generalization capability.

ABSTRACT We propose a novel physics‐guided deep learning framework for geophysical inversion that integrates Langevin Monte Carlo (LMC) sampling to quantify uncertainties in model parameters. A statistical sampling strategy is employed to enhance computational efficiency by reducing the number of required samples while preserving diversity and informativeness. The training data for the supervised learning networks are iteratively expanded with outputs from a stochastic sampler and their corresponding observed responses, ensuring representative coverage of the model space. The Jensen–Shannon divergence is adopted as the loss function for training the network model, in which the Gaussian assumption is applied to enable analytical computation. The developed workflow is evaluated on reservoir porosity inversion, where it successfully reconstructs porosity patterns in the subsurface, yielding results that closely match the reference model. Compared to traditional LMC algorithm applied to the entire data cube, the proposed approach attains substantial computational efficiency by leveraging an active learning strategy that identifies and utilizes a limited yet representative subset of the observations. The results demonstrate the effectiveness of the proposed method, highlighting its potential for application to a wide range of geophysical inverse problems.

ABSTRACT Characterization of cracks is a key issue in shale oil and gas that have become increasingly important in the hydrocarbon industry. Seismic exploration is frequently employed for the characterization of cracks in shale reservoirs. However, the accurate interpretation of seismic data for characterizing cracks in shale reservoirs remains a significant challenge, primarily due to an insufficient understanding of how subsurface pressure affects the anisotropic elastic properties of cracked shales. To address this knowledge gap, this study systematically investigates the effects of confining pressure on the anisotropic elastic properties of cracked artificial shales, with a specific focus on decoupling the distinct roles of background porosity and crack porosity. The five anisotropic elastic velocities were measured on manufactured shale samples with varying crack and background porosity, respectively, and the corresponding anisotropic parameters, Young's moduli and Poisson's ratios were derived as a function of confining pressure. The results demonstrate that the influence of crack porosity on reducing the velocities and on enhancing the elastic anisotropy is significantly more pronounced than that of background porosity. Notably, the velocities across the cracks, V p (0°) and V sh (0°), exhibit the greatest sensitivity to pressure changes, especially in samples with high crack porosity. Consequently, all the anisotropic parameters reduce exponentially with increasing confining pressure, with the reduction being most significant in shales with either the lowest background porosity or the highest crack porosity. The pressure‐dependent geomechanical properties (Young's moduli and Poisson's ratios) reveal that the direction parallel to cracks remains the most favourable path for hydraulic fracturing, particularly under low confining pressure and in rocks with high crack porosity. These findings provide critical insights for improving the quantitative interpretation of seismic data for characterizing cracks and for optimizing hydraulic fracturing design in shale reservoirs.

ABSTRACT Pre‐ and post‐stack seismic inversion is the primary approach for converting collected seismic data into geophysical property models particularly velocity for subsurface interpretation and reservoir characterization. The traditional workflows often start from an initial property model and iteratively revise it by minimizing the misfit between the acquired real seismic dataset and the synthetic one derived from the updated property models, here denoted as the soft constraint in seismic space. However, it heavily relies on human supervision in building a good initial model and monitoring the misfit optimization process. On the contrary, most of the recent deep learning‐based workflows target at non‐linearly mapping seismic patterns to properties measured at available well only, here denoted as the hard constraint in property space. Correspondingly, its accuracy greatly depends on the availability of sufficient training wells; otherwise, overfitting occurs causing the machine prediction not meeting the soft constraint throughout the target seismic survey. To resolve these limitations, this study presents a practical workflow that enables rock property inversion from partial‐stack seismic via two physics‐guided convolutional neural networks (CNNs), with the first one embedding the approximated AVO gradient to build initial property models that satisfy the hard constraint and the second one embedding the reflection coefficients to refine the models by enforcing the soft constraint. Between both CNNs is the use of well‐established relevant physics to generate pseudo property–reflectivity–seismic pairs for training the second CNN. Its added values are validated through applications to the Volve survey in North Sea and the Exmouth survey in Western Australia. The produced property volumes not only are observed of high lateral consistency and vertical resolution but also derive synthetic seismic data that are closely correlated with actual seismic data.

ABSTRACT Seismic fluid discrimination plays a critical role in sweet spot detection, reservoir characterization, reserve evaluation and well placement. Tight sandstone reservoirs are typically characterized by low porosity, poor pore connectivity, complex pore types, non‐uniform gas–water distribution and strong heterogeneity, which often lead to inaccurate fluid discrimination. In this study, we develop a double‐porosity equivalent medium model for tight sandstone reservoirs using the Keys–Xu model combined with Gassmann's equation. We systematically investigate the effects of pore structure, porosity and water saturation on elastic responses. On the basis of this model, a rock physics template (RPT) is constructed using the P‐wave modulus and the P‐ to S‐wave modulus ratio. Polynomial fitting is then applied to derive mathematical expressions for both water‐ and gas‐saturated trendlines. On the basis of these trendlines, an RPT‐based fluid indicator is defined to quantify deviations from the gas‐saturated sandstone trendline. We further apply the proposed fluid indicator to a tight gas sandstone reservoir in the central Sichuan Basin, Southwest China. The strong agreement between the extracted fluid indicator and well log‐based water saturation interpretation demonstrates that this method significantly improves the accuracy of fluid content quantification compared with traditional semi‐quantitative RPT‐based approaches. Application to seismic data further shows that our method yields a reasonable estimation of gas distribution in tight sandstone reservoirs, confirming its reliability and practical applicability for fluid characterization. This approach offers promising potential for quantifying fluid content in deep‐buried tight reservoirs.

ABSTRACT The limited understanding of rock physical properties in marine shale from the Ordovician Wulalike Formation along the western margin of the Ordos Basin has hindered comprehensive evaluations of shale gas reservoirs. This study systematically investigates the variation patterns and controlling factors of seismic elastic properties in Wulalike Formation marine shale samples through petrological and petrophysical tests, while discussing the distribution characteristics of reservoir ‘sweet spots’. Results indicate that the petrological characteristics of Wulalike Formation marine shale are influenced by tectono‐sedimentary differentiation. The lithology transitions from calcareous shale in upper slope environment to mixed shale in slope depression, and finally to siliceous shale in open marine shelf environment. Both organic matter abundance and porosity of the shale samples progressively increase with depositional environments and lithological transitions. Simultaneously, the rock stress skeleton evolves from carbonate particle dominance to clay and quartz particle dominance. Variations in rock microstructural characteristics among different lithological types of samples are the primary factor influencing seismic elastic properties. In petrophysical crossplots (impedance vs. porosity, Poisson's ratio vs. P‐wave impedance and λρ vs. μρ ), the shale samples exhibit partitioned distributions on the basis of their composition and lithology. The ‘sweet spots’ reservoirs are predominantly composed of siliceous shale, characterized by high total organic carbon (TOC), porosity and low Poisson's ratio and λρ characteristics. On the basis of the petrophysical analysis, reservoirs are categorized into three grades. Laterally, reservoir classification transitions from Grade ‘III’ to ‘I’ with changing depositional environments. Shale gas reservoirs in open marine shelf and slope depression environments (e.g., the lower part of Well ZP1) meet or exceed Grade ‘II’ standards, indicating high‐quality reservoir potential.

ABSTRACT 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.

ABSTRACT The seismoelectric effect is an electrokinetic phenomenon that arises when seismic waves propagate in water‐containing geological formations. Given that seismoelectric signals are sensitive to the hydraulic properties of the probed porous medium, they have the capability to provide important information during subsurface characterization efforts. In this work, we present a physics‐based model for the dynamic streaming potential coupling coefficient (SPCC) in partially saturated porous media. For this, we conceptualize the porous medium as a partially saturated bundle of capillary tubes. We take into account the variation of pore size to relate the capillary pressure to the water saturation in the porous medium of interest. We then up‐scale the streaming current and conduction current within the saturated capillaries under oscillatory flow conditions from pore to sample scale. The results show that the dynamic SPCC is not only a function of water saturation and the probing frequencies but also of the properties of water, mineral–water interfaces and other microstructural parameters of the porous medium. We analyse and explain the characteristics of the dynamic SPCC for two different pore size distributions (PSD): fractal and lognormal. Results show that the PSD characteristics have a strong effect on the dynamic SPCC responses. The proposed model has a remarkable ability to replicate experimental data available in the literature. In addition, it is observed that the lognormal distribution can provide a better agreement with experimental data for sand samples, which display a relatively narrow PSD. The findings of this study provide a valuable basis for interpreting seismoelectric signals under partially saturated conditions. Our proposed technique can be applied to any PSD, regardless of the complexity, providing a flexibility that is not present in alternative models found in the literature.

ABSTRACTThe transformation of elastic impedance (EI) from partial‐angle‐stacked seismic data is a crucial technique in the domains of reservoir modelling. Conventionally, EI inversion is performed on a per‐angle basis, leading to significant discrepancies in EI values across different angles, which may not accurately represent actual conditions. When the signal‐to‐noise ratio (SNR) of seismic data is low, the inverted EI tends to be unstable, resulting in poor‐quality inversion outcomes. This research proposes a novel method that allows for enabling the derivation of EI for various angles simultaneously inverted from multiple partial angle‐stack seismic datasets in one process. The aim of simultaneous inversion is to potentially ensure consistent EI results. To obtain this aim, we utilize an advanced regularization method called the Gramian constraint. Consequently, the objective function for the simultaneous inversion of multiple EIs is developed. Results from both synthetic and field data demonstrate improved stability in EI inversion, especially for the case of low SNR.