The Marchenko method can retrieve Green’s functions among virtual sources in the subsurface and receivers at the surface from single-sided reflection data. This process, called Marchenko redatuming, allows the estimation of the full-wavefield information in the inhomogeneous subsurface. The retrieved Green’s functions form the input for creating the subsurface image free of artifacts caused by internal multiples. However, when using the crosscorrelation imaging condition in Marchenko imaging, the shallower regions of the obtained image suffer from reduced resolution due to unwanted interference, which occurs in the crosscorrelation gathers at far offsets. With a simple model, we have found that the image resolution obtained using the crosscorrelation Marchenko imaging (CCMI) method is influenced by image position and the integral range of the crosscorrelation function. To overcome this problem, we develop an optimal aperture-bounded crosscorrelation imaging condition using only the constructive wavefields during integration. The optimal aperture is defined as an intercept at which the time difference between the first arrivals of up- and downgoing Green’s functions equals one wavelet period. The integration of the crosscorrelation function is then performed over the determined optimal range that varies with the image position rather than a fixed aperture. As a result, we exclude the unwanted destructive interference from the crosscorrelation gather, resulting in a high-resolution image. The effectiveness of our method has been validated with its applications to synthetic and field data tests.

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. Firstly, the method aims to reconstruct the data recorded from surface onto the target datum using Marchenko redatuming theory to obtain the response without the influence of overburden. Secondly, 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.

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.

Local full-waveform inversion (FWI) methods use redatumed seismic responses of virtual receivers within the subsurface to build the local objective function based on the convolution-type representation theorem. The Marchenko method is widely used to obtain the redatumed data. The method only requires a smoothed velocity model with correct kinematic characteristics of seismic responses for redatuming of the single-sided reflection data. However, the standard Marchenko method is insensitive to lateral propagation of the wavefield. By injecting the standard Marchenko redatumed wavefield along the boundary of the target, the local wavefield propagation modeling produces errors, which affects the accuracy of the local FWI. In this paper, a method to obtain more accurate Green’s functions is proposed by incorporating vertical seismic profile data (VSP) into the calculation process of the Marchenko source-receiver redatuming. This method allows one to obtain the accurate laterally propagating waveform, resulting in a significant improvement of lateral resolution. The proposed method is applied to a benchmark model dataset and compared with the local FWI based on standard Marchenko redatuming.

Marchenko imaging provides a powerful framework for reconstructing amplitude-preserved subsurface images while suppressing migration artifacts caused by internal multiples. This capability is achieved by applying an energy-compensated cross-correlation imaging condition to the up- and down-going Green’s functions retrieved through the Marchenko scheme. However, the performance of this approach strongly depends on the choice of the integration range: an excessively large range lowers resolution in shallow areas, whereas an overly small range can degrade the imaging quality of steeply dipping structures. To address this limitation, we propose an adaptive strategy that improves image quality by optimizing the integration range for each imaging point. The integration range is characterized by two parameters—the location of the integration center and the integration radius. Specifically, the radius is determined from the traveltime difference between the up- and down-going Green’s functions together with the depth of the imaging point, while the center location is constrained by stratal dip. The resulting optimal integration range, defined by these two parameters, is then applied to both the retrieval of Marchenko Green’s functions and the subsequent imaging. The proposed method was first tested on synthetic data, where it was shown to outperform fixed integration ranges (either too large or too small) by simultaneously enhancing shallow resolution and preserving the fidelity of steeply dipping structures. It was then further applied to field data from western China, which confirmed the feasibility of the approach.

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 base 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.