Search Results (18)

In the context of reservoir monitoring, time-lapse (4D) full-waveform inversion (FWI) of seismic data can potentially estimate reservoir changes with high resolution. However, most existing field-data applications are carried out with isotropic, and often acoustic, FWI algorithms. Here, we apply a time-lapse FWI methodology for transversely isotropic (TI) media with a vertical symmetry axis (VTI) to offshore streamer data acquired at Pyrenees field in Australia. We explore different objective functions, including those based on global correlation (GC) and designed to mitigate errors in the source signature (SI, or source-independent). The GC objective function, which utilizes mostly phase information, produces the most accurate inversion results by mitigating the difficulties associated with amplitude matching of the synthetic and field data. The SI FWI algorithm is generally more robust in the presence of distortions in the source wavelet than the other two methods, but its application to field data is hampered by reliance on amplitude matching. Taking anisotropy into account provides a better fit to the recorded data, especially at far offsets. In addition, the application of the anisotropic FWI improves the flatness of the major reflection events in the common-image gathers (CIGs). The 4D response obtained by FWI reveals time-lapse parameter variations likely caused by the reservoir gas coming out of solution and by the replacement of gas with oil. Full article

In marine seismic surveys, towed streamers record only pressure data with limited offsets and insufficient low-frequency content, whereas Ocean Bottom Nodes (OBNs) acquire multi-component data with wider offset and sufficient low-frequency content, albeit with sparser spatial sampling. Elastic full waveform inversion (EFWI) is used to estimate subsurface elastic properties by matching observed and synthetic data. However, using only towed streamer data makes it impossible to reliably estimate shear-wave velocities due to the absence of direct S-wave recordings and limited illumination. Inversion using OBN data is prone to acquisition footprint artifacts. To overcome these challenges, we propose a hierarchical joint inversion method based on P- and S-wave separation (PS-JFWI). We first derive novel acoustic-elastic coupled equations based on wavefield separation. Then, we design a two-stage inversion framework. In Stage I, we use OBN data to jointly update the P- and S-wave velocity models. In Stage II, we apply a gradient decoupling algorithm: we construct the P-wave velocity gradient by combining the gradient using PP-waves from both towed streamer and OBN data and construct the S-wave velocity gradient using the gradient using PS-waves. Numerical experiments demonstrate that the proposed method enhances the inversion accuracy of both velocity models compared with single-source and conventional joint inversion methods. Full article

The bottom-simulating reflector (BSR) serves as an important seismic indicator for identifying gas hydrate-bearing sediments. This review synthesizes global BSR observations and demonstrates that spatial relationships among BSRs, free gas, and gas hydrates frequently deviate from one-to-one correspondence. Moreover, our analysis reveals that more than 35% of global BSRs occur shallower than the bases of gas hydrate stability zones, especially in deepwater regions, suggesting that the BSRs more accurately represent the interface between the gas hydrate occurrence zone and the underlying free gas zone. BSR morphology is influenced by geological settings, sediment properties, and seismic acquisition parameters. We find that ~70–80% of BSRs occur in fine-grained, grain-displacive sediments with hydrate lenses/nodules, while coarse-grained pore-filling sediments host <20%. BSR interpretation remains challenging due to limitations in traditional P-wave seismic profiles and conventional amplitude versus offset (AVO) analysis, which hinder accurate fluid identification. To address these gaps, future research should focus on frequency-dependent AVO inversion based on viscoelastic theory, multicomponent full-waveform inversion, improved anisotropy assessment, and quantitative links between rock microstructure and elastic properties. These innovations will shift BSR research from static feature mapping to dynamic process analysis, enhancing hydrate detection and our understanding of hydrate–environment interactions. Full article

Elastic full waveform inversion (EFWI) is a crucial technique for retrieving high-resolution multi-parameter information. However, the lack of low-frequency components in seismic data may induce severe cycle-skipping phenomena in elastic full waveform inversion (EFWI). Recognizing the approximately linear relationship between the phase components of seismic data and the properties of subsurface media, we propose an Elastic Wave Phase Inversion in local-scale frequency–wavenumber domain (LFKEPI) method. This method aims to provide robust initial velocity models for EFWI, effectively mitigating cycle-skipping challenges. In our approach, we first employ a two-dimensional sliding window function to obtain local-scale seismic data. Following this, we utilize two-dimensional Fourier transforms to generate the local-scale frequency–wavenumber domain seismic data, constructing a corresponding elastic wave phase misfit. Unlike the Elastic Wave Phase Inversion in the frequency domain (FEPI), the local-scale frequency–wavenumber domain approach accounts for the continuity of seismic events in the spatial domain, enhancing the robustness of the inversion process. We subsequently derive the gradient operators for the LFKEPI methodology. Testing on the Marmousi model using a land seismic acquisition system and a simultaneous-source marine towed seismic acquisition system demonstrates that LFKEPI enables the acquisition of reliable initial velocity models for EFWI, effectively mitigating the cycle-skipping problem. Full article

The propagation of Rayleigh waves is usually accompanied by dispersion, which becomes more complex with inherent attenuation. The accurate simulation of Rayleigh waves in attenuation media is crucial for understanding wave mechanisms, layer thickness identification, and parameter inversion. Although the vacuum formalism or stress image method (SIM) combined with the generalized standard linear solid (GSLS) is widely used to implement the numerical simulation of Rayleigh waves in attenuation media, this type of method still has its limitations. First, the GSLS model cannot split the velocity dispersion and amplitude attenuation term, thus limiting its application in the Q-compensated reverse time migration/full waveform inversion. In addition, GSLS-model-based wave equation is usually numerically solved using staggered-grid finite-difference (SGFD) method, which may result in the numerical dispersion due to the harsh stability condition and poses complexity and computational burden. To overcome these issues, we propose a high-accuracy Rayleigh-waves simulation scheme that involves the integration of the fractional viscoelastic wave equation and vacuum formalism. The proposed scheme not only decouples the amplitude attenuation and velocity dispersion but also significantly suppresses the numerical dispersion of Rayleigh waves under the same grid sizes. We first use a homogeneous elastic model to demonstrate the accuracy in comparison with the analytical solutions, and the correctness for a viscoelastic half-space model is verified by comparing the phase velocities with the dispersive images generated by the phase shift transformation. We then simulate several two-dimensional synthetic models to analyze the effectiveness and applicability of the proposed method. The results show that the proposed method uses twice as many spatial step sizes and takes 0.6 times that of the GSLS method (solved by the SGFD method) when achieved at 95% accuracy. Full article

The elastic full-waveform inversion (EFWI) method efficiently utilizes the amplitude, phase, and travel time information present in multi-component seismic recordings to create detailed parameter models of subsurface structures. Within full-waveform inversion (FWI), accurate source wavelet estimation significantly impacts both the convergence and final result quality. The source wavelet, serving as the initial condition for the wave equation’s forward modeling algorithm, directly influences the matching degree between observed and synthetic data. This study introduces a novel method for estimating the source wavelet utilizing cross-correlation norm elastic waveform inversion (CNEWI) and outlines the EFWI algorithm flow based on this CNEWI source wavelet inversion. The CNEWI method estimates the source wavelet by employing normalized cross-correlation processing on near-offset direct waves, thereby reducing the susceptibility to strong amplitude interference such as bad traces and surface wave residuals. The proposed CNEWI method exhibits a superior computational efficiency compared to conventional L2-norm waveform inversion for source wavelet estimation. Numerical experiments, including in ideal scenarios, with seismic data with bad traces, and with multi-component data, validate the advantages of the proposed method in both source wavelet estimation and EFWI compared to the traditional inversion method. Full article

Seismic characterization of landslides offers the potential for developing high-resolution models on subsurface shear-wave velocity profile. However, seismic methods based on reflection processing are challenging to apply in such scenarios as a consequence of the disturbance to the often well-defined structural and stratigraphic layering by the landslide process itself. We evaluate the use of alternative seismic characterization methods based on elastic full waveform inversion (E-FWI) to probe the subsurface of a landslide complex in Majes, southern Peru, where recent agricultural development and irrigation activities have altered the hydrology and groundwater table and are thought to have contributed to increased regional landslide activities that present continuing sustainability community development challenges. We apply E-FWI to a 2D near-surface seismic data set for the purpose of better understanding the subsurface in the vicinity of a recent landslide location. We use seismic first-arrival travel-time tomography to generate the inputs required for E-FWI to generate the final high-resolution 2D compressional- and shear-wave (P- and S-wave) velocity models. At distances greater than 140 m from the cliff, the inverted models show a predominantly vertically stratified velocity structure with a low-velocity near-surface layer between 5–15 m depth. At distances closer than 140 m from the cliff, though, the models exhibit significantly reduced shear-wave velocities, stronger heterogeneity, and localized shorter wavelength structure in the top 20 m. These observations are consistent with those expected for a recent landslide complex; however, follow-on geotechnical analysis is required to confirm these assertions. Overall, the E-FWI seismic approach may be helpful for future landslide characterization projects and, when augmented with additional geophysical and geotechnical analyses, may allow for improved understanding of the hydrogeophysical properties associated with suspected ground-water-driven landslide activity. Full article

Subsurface characterization is crucial for geothermal energy exploration and production. Yet hydrothermal reservoirs usually reside in highly fractured and faulted zones where accurate characterization is very challenging because of low signal-to-noise ratios of land seismic data and lack of coherent reflection signals. We perform an active-source seismic characterization for the Blue Mountain geothermal field in Nevada using active seismic data to reveal the elastic medium property complexity and fault distribution at this field. We first employ an unsupervised machine learning method to attenuate groundroll and near-surface guided-wave noise and enhance coherent reflection and scattering signals from noisy seismic data. We then build a smooth initial P-wave velocity model based on an existing magnetotellurics survey result, and use 3D first-arrival traveltime tomography to refine the initial velocity model. We then derive a set of elastic wave velocities and anisotropic parameters using elastic full-waveform inversion, and obtain PP and PS images using elastic reverse-time migration. We identify major faults by analyzing the variations of seismic velocities and anisotropy parameters, and reveal mid- to small-scale faults by applying a supervised machine learning method to the seismic migration images. Our characterization reveals complex velocity heterogeneities and anisotropies, as well as faults, with a high spatial resolution. These results can provide valuable information for optimal placement of future injection and production wells to increase geothermal energy production at the Blue Mountain geothermal power plant. Full article

The prediction of subsurface properties such as velocity, density, porosity, and water saturation has been the main focus of petroleum geosciences. Advanced methods such as Full Waveform Inversion (FWI), Joint Migration Inversion (JMI) and ML-Rock Physics are able to produce better predictions than their predecessors, but they still require tedious manual interpretation that is prone to human error. The research on these methods remains open as they suffer from technical limitations. As computing resources are becoming cheaper, the use of a single deep-generative adversarial network is feasible in predicting all these properties in a completely data-driven manner. In our proposed method of multiscale pix2pix applied to SEG SEAM salt data, we have managed to map from one input, which is seismic post-stack data, to several outputs of reservoir and elastic properties such as porosity, velocity, and density by using only one trained model and without having to manually interpret or pre-process the input data. With 90% accuracy of the results in the synthetic data testing, the method is worthy of being explored by the petroleum geoscience fraternity. Full article

Strong-scattering medium can usually form a good sealing medium for oil and gas resources. However, conventional elastic full-waveform inversion (EFWI) methods are difficult to build reliable velocity models under the condition of lacking low-frequency information. The elastic direct envelope inversion (EDEI) method has been proven to be able to model large-scale Vp and Vs structures of strong-scattering media. The successive use of EDEI and EFWI can obtain fine structures of the strong scatterers and their shielding areas. However, the inversion effects of inner velocity and bottom boundaries of strong scatterers by the existing methods need to be improved. In this paper, we propose the elastic direct envelope inversion with anisotropic total variation constraint (EDEI-ATV). The anisotropic total variation (ATV) constraint has the advantage of making the velocity more uniform inside the layer and sharper on boundaries, which can be used to improve the inversion results of EDEI. During the iterations, the ATV constraint is directly applied to the update of Vp and Vs, and the alternately iterative algorithm can achieve good results. After obtaining reliable large-scale Vp and Vs structures, the EFWI with anisotropic total variation constraint (EFWI-ATV) is performed to obtain high-precision Vp and Vs structures. Numerical examples verify the effectiveness of the proposed method. Full article

Elastic full waveform inversion (EFWI) is essential for obtaining high-resolution multi-parameter models. However, the conventional EFWI may suffer from severe cycle skipping without the low-frequency components in elastic seismic data. To solve this problem, we propose a multistage phase correction-based elastic full waveform inversion method in the frequency-wavenumber domain, which we call PC-EFWI for short. Specifically, the seismic data are first split using 2-D sliding windows; for each window, the seismic data are then transformed into the frequency-wavenumber domain for PC-EFWI misfit. In addition, we introduced a phase correction factor in the PC-EFWI misfit. In this way, it is possible to reduce phase differences between measured and synthetic data to mitigate cycle skipping by adjusting the phase correction factor in different scales. Numerical examples with the 2-D Marmousi model demonstrate that the frequency-wavenumber domain PC-EFWI with multistage strategy is an excellent way to reduce the risk of EFWI cycle skipping and build satisfactory start models for the conventional EFWI. Full article

Quantitative dynamic strain measurements of the ground would be useful for engineering scale problems such as monitoring for natural hazards, soil-structure interaction studies, and non-invasive site investigation using full waveform inversion (FWI). Distributed acoustic sensing (DAS), a promising technology for these purposes, needs to be better understood in terms of its directional sensitivity, spatial position, and amplitude for application to engineering-scale problems. This study investigates whether the physical measurements made using DAS are consistent with the theoretical transfer function, reception patterns, and experimental measurements of ground strain made by geophones. Results show that DAS and geophone measurements are consistent in both phase and amplitude for broadband (10 s of Hz), high amplitude (10 s of microstrain), and complex wavefields originating from different positions around the array when: (1) the DAS channels and geophone locations are properly aligned, (2) the DAS cable provides good deformation coupling to the internal optical fiber, (3) the cable is coupled to the ground through direct burial and compaction, and (4) laser frequency drift is mitigated in the DAS measurements. The transfer function of DAS arrays is presented considering the gauge length, pulse shape, and cable design. The theoretical relationship between DAS-measured and pointwise strain for vertical and horizontal active sources is introduced using 3D elastic finite-difference simulations. The implications of using DAS strain measurements are discussed including directionality and magnitude differences between the actual and DAS-measured strain fields. Estimating measurement quality based on the wavelength-to-gauge length ratio for field data is demonstrated. A method for spatially aligning the DAS channels with the geophone locations at tolerances less than the spatial resolution of a DAS system is proposed. Full article

Injecting grout into the gaps between tunnel shield segments and surrounding rocks can reduce ground subsidence and prevent ground water penetration. However, insufficient grouting and grouting defects may cause serious geological disasters. Ground penetrating radar (GPR) is widely used as a nondestructive testing (NDT) method to evaluate grouting quality and determine the existence of defects. This paper provides an overview of GPR applications for grouting defect detection behind tunnel shield segments. State-of-the-art methodologies, field cases, experimental tests and signal processing methods are discussed. The reported field cases and model test results show that GPR can detect grouting defects behind shield tunnel segments by identifying reflected waves. However, some subsequent problems still exist, including the interference of steel bars and small differences in the dielectric constants among media. Recent studies have focused on enhancing the signal-to-noise ratio and imaging methods. Advanced GPR signal processing methods, including full waveform inversion and machine learning methods, are promising for detecting imaging defects. Additionally, we conduct a preliminary experiment to investigate environmental noise, antenna configuration and coupling condition influences. Some promising topics, including multichannel configuration, rapid evaluation methods, elastic wave method scanning equipment for evaluating grout quality and comprehensive NDT methods, are recommended for future studies. Full article

Carbonate reservoirs have significant reserves globally, but the substantial heterogeneity brings intractable difficulties to exploration. In the work area, the thick salt rock reduces the resolution of pre-salt seismic signals and increases the difficulty of reservoir characterization. Therefore, this paper proposes to utilize wavelet frequency decomposition technology to depict the seismic blank reflection area’s signal and improve the pre-salt signal’s resolution. The high-precision pre-stack inversion based on Bayesian theory makes full use of information from various angles and simultaneously inverts multiple elastic parameters, effectively depicting reservoirs with substantial heterogeneity. Integrating the high-precision inversion results and the Kuster-Toksöz model, a porosity prediction method is proposed. The inversion results are consistent with the drilling rock samples and well-logging porosity results. Moreover, the reef’s accumulation and growth, which conform to the geological information, proves the accuracy of the above methods. This paper also discusses the seismic reflection characteristics of reefs and the influence of different lithological reservoirs on the seismic waveform response characteristics through forward modeling, which better proves the rationality of porosity inversion results. It provides a new set of ideas for future pre-salt carbonate reef reservoirs’ prediction and characterization methods. Full article

The depth velocity model is a critical element for providing seismic data processing success, as it is responsible for the times of waves’ propagation and, therefore, prescribes the location of geological objects in the resulting seismic images. Constructing a deep velocity model is the most time-consuming part of the entire seismic data processing, which usually requires interactive human intervention. This article introduces the consistently numerical method for reconstructing a depth velocity model based on the modified version of the elastic Full Waveform Inversion (FWI). The specific feature of this approach to FWI is the decomposition of the space of admissible velocity models into subspaces of propagator (macro velocity) and reflector components. In turn, the latter transforms to the data space reflectivity on the base of migration transformation. Finally, we perform minimisation in two different spaces: (1) Macro velocity as a smooth spatial function; (2) Migration transforms data space reflectivity to the spatial reflectivity. We present numerical experiments confirming less sensitiveness of the modified version of FWI to the lack of the low time frequencies in the data acquired. In our computations, we use synthetic data with valuable time frequencies from 5 Hz. Full article