Seismic diffractions are the responses of small-scale discontinuous structures. They contain subwavelength geologic information. Thus, diffractions can be used for high-resolution imaging. The energy of diffractions is generally much weaker than that of reflections. Therefore, diffracted energy is typically masked by specular reflected energy. Diffraction/reflection separation is a crucial preprocessing step for diffraction imaging. To resolve the diffraction-separation problem, we have developed a method based on the multichannel singular-spectrum analysis (MSSA) algorithm for diffraction separation by reflection suppression. The MSSA algorithm uses the differences in the kinematic and dynamic properties between reflections and diffractions to suppress time-linear signals (reflections) and separate weaker time-nonlinear signals (diffractions) in the common-offset or poststack domain. For the time-linear signals, the magnitudes of the singular values are proportional to the energy strength of the signals. The stronger the energy of a component of the linear signals is, the larger the corresponding singular values will be. The singular values of reflections and diffractions have dissimilar spatial distributions in the singular-value spectrum because of the differences in their linear properties and energy. Only the singular values representing diffractions are selected to reconstruct seismic signals. Synthetic data and field data are used to test our method. The results reveal the good performance of the MSSA algorithm in enhancing diffractions and suppressing reflections.

Seismic weak responses from subsurface small-scale geologic discontinuities or inhomogeneities are encoded in 3D diffractions. Separating weak diffractions from a strong reflection background is a difficult problem for diffraction imaging, especially for the 3D case when they are tangent to or interfering with each other. Most conventional diffraction separation methods ignore the azimuth discrepancy between reflections and diffractions when suppressing reflections. In fact, the reflections associated with a specific pair of azimuth-dip angle possess sparse characteristics, and the diffractions adhering to Huygens’ principle behave as low-rank components. Therefore, we have developed a 3D low-rank diffraction imaging method that uses the Mahalanobis-based low-rank and sparse matrix decomposition method for separating and imaging 3D diffractions in the azimuth-dip angle image matrix. The advantages of our 3D diffraction imaging method not only includes the handling of interfering events but also includes ensuring a better protection of weak diffractions. The numerical experiment illustrates the good performance of our method in imaging small-scale discontinuities and inhomogeneities. The field data application of carbonate reservoirs further confirms its potential value in resolving the masked small-scale cavities that can provide storage spaces and a migration pathway for petroleum.

Separating weak diffractions in the prestack domain plays a vital role in high-resolution imaging small-scale discontinuities or inhomogeneities, which are associated with the migration paths and storage spaces for carbonate fracture-cave reservoirs. A shot-domain diffraction separation method is proposed with a SVMF (space-varying media filter) in the flattened shot domain. The diffractions with fast dynamic changings, including damping amplitudes and phase reversal, are filtered out by the spacing-varying median filter along the prediction-step directions of the flattened shot domain. The proposed method opens a possibility for separating diffractions in the shot domain and has an advantage in the traditional common-offset diffraction separation method. A numerical experiment illustrates the good performance of the proposed method in eliminating reflections and separating diffractions in the shot domain, and the field application further verifies its potential value in resolving the hidden small-scale cavities and tiny faults in carbonate reservoirs.

Seismic diffractions contain valuable information regarding small-scale inhomogeneities or discontinuities, and therefore they can be used for seismic interpretation in the exploitation of hydrocarbon reservoirs. Velocity analysis is a necessary step for accurate imaging of these diffractions. A new method for diffraction velocity analysis and imaging is proposed that uses an improved adaptive minimum variance beamforming technique. This method incorporates the minimum variance, coherence factor, and correlation properties to improve the signal-to-noise ratio and enhance correlations. Our method can make seismic diffractions become better focused in semblance panels, allowing for the optimal migration velocity for diffractions to be accurately picked. Synthetic and field examples demonstrate that the migration velocity for the diffractions can differ from that for the reflections. The results suggest that the diffraction velocity analysis and imaging method is feasible for accurately locating and identifying small-scale discontinuities, which leads to the possibility of using this approach for practical application and seismic interpretation.

Seismic diffractions provide high-resolution details of small-scale geologic discontinuities, and diffraction imaging can be an important contribution to the exploration of faults, fractures, and cavities. However, reflections with strong energy generally mask the existence of weak diffractions in seismic records, and separating diffractions is necessary to see the full benefit of diffraction imaging. Here, a modified convolutional neural network (CNN) is used for separating diffractions. The input wavefields are modeled as the summation of individual diffractions and reflections, the network parameters are learned from multiple input data instances by minimizing the distance between the output of the network and the diffractions, and reflections are implicitly removed. To enhance the diffraction separation performance, we have used dilated convolutions to aggregate diffraction information, then combined batch normalization and residual learning to speed up the training process. The modified CNN can realize adaptive diffraction separation and avoid setting parameters in the subsequent application process. The synthetic Sigsbee2A model demonstrates the performance of the proposed method in removing high-slope reflections and imaging small-scale scatterers. The application to the Nankai field data further illustrates the ability of the methods to extract weak diffractions beneath the interference of other waves, revealing potential reservoir-related geologic features.