Full-waveform inversion (FWI) is increasingly used in land seismic exploration to achieve high-resolution near-surface models. In complex near-surface environments, however, FWI is challenged by noisy data and inaccurate initial models, raising the need for an effective regularization strategy to mitigate the inherent ill-posedness of FWI. Incorporating geological information, such as structural dips, into the regularization operator, has proven effective in stabilizing FWI and promoting geologically meaningful results. Obtaining clear seismic images that provide structural insights is, nonetheless often hindered in complex near-surface surveys due to noisy reflection data and gradient weathering velocity. Structural regularization treats imaged reflectors as velocity contours and penalizes velocity variations along the dips. We propose a novel approach involving deformable-layer tomography (DLT) to directly invert for velocity contour distributions. DLT is well suited for near-surface applications as it accommodates both gradient velocity variations and abrupt velocity contrasts. In order to avoid erroneous structural regularization, we evaluate the accuracy of the DLT-estimated dips and assign weights accordingly. The weighted dip field is used to construct directional total variation (TV) in regularizing FWI, using the edge-preserving smoothing of TV regularization as well as the dip constraint. Using a realistic near-surface model, we demonstrate that FWI with the DLT-guided directional TV regularization outperforms conventional TV regularization. Our findings underscore the advantages of incorporating geologic structural constraint into FWI under complex near-surface conditions, highlighting the improved fidelity and resolution in near-surface velocity model building.

Marchenko imaging has the particular ability to generate the subsurface image free of spurious artifacts related to internal multiples. However, conventional Marchenko imaging (C-MI) is performed based on the assumption that the sources and receivers of the recorded seismic data are placed on a flat surface, which is restrictive and hard to satisfy in mountainous areas with rugged topography. The elevation-static correction (or time shift) is a usual solution to ensure this assumption holds. But it only works well if the surface consistency is satisfied. To alleviate the limitations of C-MI in processing seismic data acquired from mountainous areas with rugged topography, we present a technique for conducting Marchenko imaging from a floating datum. The proposed Topography-Marchenko imaging (T-MI) is achieved by estimating an initial down-going focusing function between a floating datum and a focal point in the subsurface. In this work, we use seismic data corrected to a floating datum rather than a final datum as input to the iterative Marchenko scheme to retrieve Green’s functions. The retrieved Marchenko Green’s functions are further used to generate the subsurface image. The T-MI method can effectively avoid imaging distortions caused by the elevation-static correction. The proposed T-MI method is validated through applications to a synthetic model with rugged topography and a land dataset acquired from a mountainous area in Northwest Sichuan, China.

When seismic waves propagate through hydrocarbon reservoirs, there is a significant attenuation of high-frequency energy. The quality factor Q is usually treated as a sensitive parameter indicating oil and gas reservoirs, but extracting seismic attenuation parameters using the conventional time-frequency domain methods often yields unsatisfactory results. In order to improve the accuracy of Q estimation, we propose a new method for calculating seismic parameters. We first introduce a Gaussian window into the general linear chirplet transform (GLCT) based on dominant frequency weight, which is now used in the W-transform (WT). This modification enables the improved GLCT (IGLCT) to adjust its time-frequency resolution according to the changes at the seismic dominant frequency, thereby enhancing the time resolution of conventional GLCT. Subsequently, based on the characteristics of high-frequency energy attenuation anomalies in seismic records and in combination with the Teager-Kaiser energy operator (TKEO), then the effective estimation of seismic attenuation parameters is achieved. This method can obtain a higher resolution time-frequency spectrum compared to conventional time-frequency analysis (TFA) methods. In addition, the TKEO used in this technique has significant instantaneous features, which can be used to accurately estimate the energy of monofrequency signals. Finally, seismic attenuation parameters are calculated based on the quantified definition of the quality factor Q and the relative energy attenuation within the given time window. Theoretical model testing and actual data processing indicate that the proposed method for estimating seismic attenuation parameters has the capability for identifying the thin layers and can effectively predict the hydrocarbon reservoirs.

The Marchenko method has received significant attention in geophysics due to its specific ability to retrieve the accurate Green’s functions directly from data without a subsurface focal point to have an actual physical receiver located at it. However, its application capacity in anisotropic medium remains unexplored. Given the increasing complexity of exploration tasks, it has become imperative to investigate its feasibility of deploying the Marchenko method in anisotropic media. This study aims to assess the applicability of the Marchenko method in retrieving Green’s functions in anisotropic medium with a synthetic dataset simulated over a tilted transversely isotropic (TTI) model. The Green’s functions are retrieved based on the assumptions that the data are acquired from either an isotropic or TTI medium model. A comparison analysis reveals that the first arrivals obtained based on the TTI medium assumption provides more accurate travel times and amplitudes, resulting in a more accurate reconstruction of the Green’s function. The study demonstrates that Marchenko method can be effectively applied in anisotropic medium.

The least-squares reverse time migration (LSRTM) can obtain high resolution and true amplitude imaging results. However, LSRTM for full model domain data requires simulation throughout the entire model space, resulting in significant computational costs. In addition, the Born approximation, which is based on single scattering theory, can cause imaging artifacts by treating multiple reflections from the overburden as primary reflections. To address these issues, the Marchenko redatuming method can be used to separate the influence of the overburden. However, due to factors such as acquisition aperture, phase and amplitude errors can occur in far offset Marchenko redatumed data. Therefore, a prestack correlative LSRTM method based on Marchenko redatumed data is proposed for target-oriented imaging, which includes two key points. First, the method aims to reconstruct the data recorded from the surface onto the target datum using the Marchenko redatuming theory to obtain the response without the influence of overburden. Second, by constructing the prestack normalized zero-lag cross-correlation error function, the optimal reflection coefficient model is found for each shot’s record, and the final imaging result is generated by stacking the optimal reflection coefficient model of all redatumed data, thereby alleviating the problem of the incoherent stacking at far offset in redatumed data, which reduces the imaging quality. Numerical examples demonstrate that the proposed method has higher computational efficiency and can provide imaging results with fewer artifacts.

In complex near-surface geologic settings, first-arrival traveltime tomography suffers from strong data noise and uneven raypath coverage. To reduce the inversion nonuniqueness, model smoothing is necessary but often without incorporating structural constraints based on seismic images. The reasons include a lack of shallow reflections, strong noise, and gradient velocity field with strong lateral variation. We propose to estimate the near-surface structural trend from the velocity solution of deformable-layer tomography (DLT), that inverts for velocity contours directly. Then the structural dips derived from the DLT solution are taken as the structural regularization in the subsequent traveltime tomographic inversion. Tests for the 2011 Symposium on the Application of Geophysics to Engineering and Environmental Problems (SAGEEP) workshop model illustrate that this DLT-guided structural regularization steers the tomographic inversion toward more accurate near-surface models. The application of the DLT-guided structural regularization to field data reveals the near-surface geology of a beach area in Qingdao, China. The DLT solution could complement seismic images or other structural information in regularizing tomographic inversion.

The inadequate resolution of cavity karst reservoir characterization is a key factor affecting the hydrocarbon production efficiency of ultradeep marine carbonates. Full waveform inversion (FWI) using high-frequency seismic data can provide a higher resolution than conventional methods. However, computational efficiency limits its application. This letter proposed a target-oriented local FWI method for cavity karst reservoir characterization. Based on the wavefield injection and Marchenko redatuming methods, a local wavefield forward modeling operator is derived. It can obtain the local wavefield corresponding to the physical source at the surface. Based on this local wavefield reconstruction, an objective function of the local FWI is established. To enhance the reservoir boundaries in the inversion results, a stabilizing strategy that combines total variation (TV) and minimum support (MS) regularization is proposed. A synthetic data test demonstrates that the obtained model can well describe the cavity karst reservoir structures.

Conventional amplitude versus offset (AVO) inversion methods are mainly based on various Zoeppritz approximations. The assumptions of small contrast and linear relationship lead to the most inversion methods being difficult to have high inversion accuracy. In this article, the exact Zoeppritz equation is used to establish the prestack inversion method under the Bayesian framework. It integrates multisource information to generate posterior distributions of P-, S-wave velocity and density. In the Bayesian theory, the prior model works as the regularization term which has a strong effect on the inversion results. The strategy to obtain a relatively accurate prior model can improve the inversion accuracy. Therefore, an exact Zoeppritz equation based nonlinear AVO inversion algorithm combing whale optimization particle filtering (WOPF) is proposed. The WOPF method can generate a relatively stable and accurate initial model for the Bayesian prestack inversion. We validate the new method through two synthetic models and field data. Comparisons are made with the conventional linear and nonlinear AVO inversion methods. The results show that the proposed method can provide much more accurate inverted elastic parameters in different geological conditions.