Prestack Images Research Articles

Robust prestack seismic facies analysis using shearlet transform-based deep learning

Abstract One of the primary purposes of seismic stratigraphy is to evaluate the components of seismic layer relationships within a depositional chronology. Prestack seismic images contain a wealth of information, such as variations in the offset and azimuth of a seismic event, and naturally produce higher-resolution seismic facies analysis results than poststack data. However, prestack data usually suffer from potential unreliability issues due to low signal-to-noise ratios. As this is often overlooked, the present facies analysis methods sometimes fail to extract accurate features from prestack images, which inevitably influences the facies analysis results. To address this issue, this article provides a robust data-driven technique for extracting offset-temporal features via shearlet transform-based deep convolution autoencoders (STCAEs). Unlike the present facies analysis in the time domain, STCAE can optimally represent prestack images at multiple scales and directions through the two-dimensional shearlet transform, which preserves fine edges while suppressing noise in prestack images. Subsequently, robust features are extracted from prestack images in a data-driven manner through a contractive convolutional autoencoder network. We compare our method with other advanced methods and demonstrate the advantages of the proposed approach in classifying seismic layers in prestack data.

Self-Supervised Ground-Roll Noise Attenuation Using Self-Labeling and Paired Data Synthesis

Seismic exploration is a complex process that depends on different sources of information. An essential one is seismic imaging, and much of its interpretation performance relies on high-quality processing, which is currently still very dependent on prone-to-error human mediation. Automation of such processing steps is necessary to reduce the amount of time to treat seismic data—usually months—and improve the outcome overall quality by reducing the inherent subjectivity in the process. One of the most critical steps in seismic processing is noise suppression, and ground roll is one of the most challenging and everyday noises observed in seismic prestack data. In this article, we propose a self-supervised two-step approach to attenuate ground-roll noise in seismic prestack images. First, we detect ground-roll-affected area using convolutional neural networks, and then, we filter ground-roll noise in the detected area using conditional generative adversarial networks (cGANs). For each of these steps, we propose to build paired noisy/noise-free training sets with no supervision or reference data, hence creating a self-supervised pipeline for filtering ground-roll noise. Our two-stage approach enables noise suppression in the affected area while preserving the signal in unaffected areas. In addition, we propose to refactor conventional qualitative metrics in the industry into quantitative scores disregarding any reference data to evaluate ground-roll suppression for different geologies and report reliable results compared with expert filtering.

Interpretation value of diffractions and sub-specular reflections – applications on the Zhao Dong field

We provide an overview of integrated pre-stack depth migration and diffraction imaging for the Zhao Dong field, Bohai Bay, China. This field is highly compartmentalized by complex faulting and further characterized by channel systems, fractures and volcanic features. The objective of the diffraction imaging is to better define these small-scale features. Tools to facilitate interpretation include displays with pre-stack depth migration and diffraction images overlain in different colour scales, as well as a weighted blending of them into a single volume. An important concept is that of the sub-specular reflection, which is obtained alongside the pure diffraction image by applying ultra-weak specularity tapers. Tuning properties of elementary diffractor images together with sub-specular reflectors provide a decisive uplift of diffraction imaging for the interpreter.

Imaging diffractors using wave-equation migration

We have developed a fast and practical wave-equation-based migration method to image subsurface diffractors. The method is composed of three steps in our implementation. First, it decomposes extrapolated receiver wavefields at every imaging point into local plane waves by a linear Radon transform; the transform is realized by a novel computationally efficient recursive algorithm. Second, the decomposed plane waves are zero lag-correlated with the incident source wavefields, where the incident angles are computed via the structure tensor approach. The resulting prestack images are binned into dip-angle gathers according to the directions of the decomposed plane waves and the calculated incident angles. Third, a windowed median filter is applied to the dip-angle gathers to suppress the focused reflection energy, and it produces the desired diffraction images. This method is tested on synthetic and field data. The results demonstrate that it is resistant to random noise, computationally efficient, and applicable to field data in practice. The results also indicate that the diffraction images are able to provide important discontinuous geologic features, such as scattering and faulting zones, and thus are helpful for seismic interpretation.

Plane-wave least-squares reverse-time migration

A plane-wave least-squares reverse-time migration (LSRTM) is formulated with a new parameterization, where the migration image of each shot gather is updated separately and an ensemble of prestack images is produced along with common image gathers. The merits of plane-wave prestack LSRTM are the following: (1) plane-wave prestack LSRTM can sometimes offer stable convergence even when the migration velocity has bulk errors of up to 5%; (2) to significantly reduce computation cost, linear phase-shift encoding is applied to hundreds of shot gathers to produce dozens of plane waves. Unlike phase-shift encoding with random time shifts applied to each shot gather, plane-wave encoding can be effectively applied to data with a marine streamer geometry. (3) Plane-wave prestack LSRTM can provide higher-quality images than standard reverse-time migration. Numerical tests on the Marmousi2 model and a marine field data set are performed to illustrate the benefits of plane-wave LSRTM. Empirical results show that LSRTM in the plane-wave domain, compared to standard reverse-time migration, produces images efficiently with fewer artifacts and better spatial resolution. Moreover, the prestack image ensemble accommodates more unknowns to makes it more robust than conventional least-squares migration in the presence of migration velocity errors.

Imaging diffraction points using the local image matrices generated in prestack migration

Energy-angle distributions in the local image matrix, which is a function of local incident and receiving angles at a subsurface point, are different for a planar reflector and a diffraction point. The former exhibits a linear distribution along a certain dip direction, whereas the latter shows a scattered distribution in the entire matrix. Therefore, the singular values of the local image matrix in the local angle domain indicate the energy distribution along different dip directions. The difference between the auto- and crosscorrelation coefficients of the sets of singular values between adjacent image points can be used to distinguish these two types of targets and obtain prestack images which contain only diffraction points. Using three synthetic examples, we found that the method can effectively image diffraction points. Seismic images of diffraction points may provide important information about geological discontinuities and diffraction points can be taken as secondary sources for potential applications in imaging subsalt structures which are poorly illuminated by primary waves.

Time-shift imaging condition in seismic migration

Seismic imaging based on single-scattering approximation is in the analysis of the match between the source and receiver wavefields at every image location. Wavefields at depth are functions of space and time and are reconstructed from surface data either by integral methods (Kirchhoff migration) or by differential methods (reverse-time or wavefield extrapolation migration). Different methods can be used to analyze wavefield matching, of which crosscorrelation is a popular option. Implementation of a simple imaging condition requires time crosscorrelation of source and receiver wavefields, followed by extraction of the zero time lag. A generalized imaging condition operates by crosscorrelation in both space and time, followed by image extraction at zero time lag. Images at different spatial crosscorrelation lags are indicators of imaging accuracy and are also used for image-angle decomposition. In this paper, we introduce an alternative prestack imaging condition in which we preserve multiple lags of the time crosscorrelation. Prestack images are described as functions of time shifts as opposed to space shifts between source and receiver wavefields. This imaging condition is applicable to migration by Kirchhoff, wavefield extrapolation, or reverse-time techniques. The transformation allows construction of common-image gathers presented as functions of either time shift or reflection angle at every location in space. Inaccurate migration velocity is revealed by angle-domain common-image gathers with nonflat events. Computational experiments using a synthetic data set from a complex salt model demonstrate the main features of the method.

Least-squares joint imaging of multiples and primaries

Multiple reflections contain subsurface reflectivity information which often complements that found in primary reflections. Previous attempts to combine the additional information by summing prestack images of the primaries and multiples have generally failed because of crosstalk leakage between the images. We present a general linear least-squares joint imaging of multiples-and primaries (LSJIMP) inversion method to simultaneously suppress crosstalk noise and combine pegleg multiples and primaries in a prestack sense. In general, LSJIMP is compatible with a wide variety of prestack imaging methods and can be extended to jointly image primaries and other embedded wave modes such as shear-wave conversions. We present a particular LSJIMP implementation that utilizes an efficient linear operator to model and image pegleg multiples in a true relative-amplitude sense. Applied to a given type of pegleg multiple in the data, our imaging operator produces an image directly comparable to primaries after NMO. Our operator's kinematic component is an extension of the NMO equation that independently images split peglegs in a moderately heterogeneous earth. Its amplitude component corrects multiples for their differences in angle-dependent reflection strength and illumination, relative to a primary. We test our LSJIMP implementation on 2D and 3D prestack field data examples and show that the method cleanly separates primaries and multiples and also uses joint information in the events to interpolate the signal in acquisition gaps.

Smiles and frowns in migration/velocity analysis

Reliable seismic depth migrations require an accurate input velocity model. Inaccurate velocity estimates will distort point diffractors into smiles or frowns on a depth section. For both poststack and prestack migrated sections, high velocities cause deep smiles while low velocities cause shallow frowns on migrated gathers. However, for prestack images in the offset domain, high velocities cause deep frowns while low velocities cause shallow smiles. If the velocity is correct, there will be no variation in the depth migration as a function of offset and no smiles or frowns in the offset domain. We explain migration responses both mathematically and graphically and thereby provide the basis for depth migration velocity analysis.

Overthrust imaging with tomo‐datuming: A case study

Through the use of iterative turning‐ray tomography followed by wave‐equation datuming (or tomo‐datuming) and prestack depth migration, we generate accurate prestack images of seismic data in overthrust areas containing both highly variable near‐surface velocities and rough topography. In tomo‐datuming, we downward continue shot records from the topography to a horizontal datum using velocities estimated from tomography. Turning‐ray tomography often provides a more accurate near‐surface velocity model than that from refraction statics. The main advantage of tomo‐datuming over tomo‐statics (tomography plus static corrections) or refraction statics is that instead of applying a vertical time‐shift to the data, tomo‐datuming propagates the recorded wavefield to the new datum. We find that tomo‐datuming better reconstructs diffractions and reflections, subsequently providing better images after migration. In the datuming process, we use a recursive finite‐difference (FD) scheme to extrapolate wavefield without applying the imaging condition, such that lateral velocity variations can be handled properly and approximations in traveltime calculations associated with the raypath distortions near the surface for migration are avoided. We follow the downward continuation step with a conventional Kirchhoff prestack depth migration. This results in better images than those migrated from the topography using the conventional Kirchhoff method with traveltime calculation in the complicated near surface. Since FD datuming is only applied to the shallow part of the section, its cost is much less than the whole volume FD migration. This is attractive because (1) prestack depth migration usually is used iteratively to build a velocity model, so both efficiency and accuracy are important factors to be considered; and (2) tomo‐datuming can improve the signal‐to‐noise (S/N) ratio of prestack gathers, leading to more accurate migration velocity analysis and better images after depth migration. Case studies with synthetic and field data examples show that tomo‐datuming is especially helpful when strong lateral velocity variations are present below the topography.

Wave front construction in smooth media for prestack depth migration

We implemented a wave front construction algorithm specifically designed for smooth media for application to prestack depth migration. The highest priority was given to maximum computational speed to allow an extension of the techniques to 3D media. A simple grid-based model representation in combination with fast bilinear interpolation is used. It is shown that this procedure has no distorting effects on the ray tracing results for smooth media. In our implementation, wave front construction (WFC) has proven to be as fast as some of the recently developed methods for travel time computations. WFC has advantages over these methods, since amplitudes and other ray theoretical quantities are available, and it is not restricted to the calculation of only first arrivals. Thus, it meets the requirements for migration in complex media. Furthermore, WFC allows for introduction of a perturbation scheme for computing travel times for slightly varying models simultaneously. This has applications for, e.g., prestack velocity estimation techniques. The importance of later arrivals for migration in complex media is demonstrated by prestack images of the Marmousi data set.