Polarity reversals in PS and SP images of elastic reverse time migration will cause destructive interference in the final stacked results, which may lead to an erroneous interpretation. We derive a polarity reversal correction based on an analysis of the polarity distribution of PS and SP images. The key aspect for a polarity reversal correction is the polarity distribution of the S-wave component. Polarity reversal correction methods can be performed according to the actual patterns of the polarity distribution in gathers. For the 2D isotropic case, a sign factor is introduced to represent the polarity distribution of the S-wave component; this sign factor can be computed using the energy flux density vector. We present a procedure to correct the polarity reversal for elastic reverse time migration in the common-shot domain. First, the sign factor is computed during the wavefield reconstruction for every imaging point. Then, the polarity reversal is corrected by multiplying the PS and SP images with the sign factor at every time step when an elastic imaging condition is applied. We also design a filter algorithm for the sign factor to improve its consistency along an event and thereby to diminish the impact of the inaccuracy of the energy flux density vector and to improve the imaging results. Numerical examples have shown that the polarity reversal correction procedure works and that the sign filter algorithm successfully eliminates the consequences of the inaccuracy of the energy flux density vector. The quality of the migration events is effectively improved.

The scalar images (PP, PS, SP, and SS) of elastic reverse time migration (ERTM) can be generated by applying an imaging condition as crosscorrelation of pure wave modes. In conventional ERTM, Helmholtz decomposition is commonly applied in wavefield separation, which leads to a polarity reversal problem in converted-wave images because of the opposite polarity distributions of the S-wavefields. Polarity reversal of the converted-wave image will cause destructive interference when stacking over multiple shots. Besides, in the 3D case, the curl calculation generates a vector S-wave, which makes it impossible to produce scalar PS, SP, and SS images with the crosscorrelation imaging condition. We evaluate a vector-based ERTM (VB-ERTM) method to address these problems. In VB-ERTM, an amplitude-preserved wavefield separation method based on decoupled elastic wave equation is exploited to obtain the pure wave modes. The output separated wavefields are both vectorial. To obtain the scalar images, the scalar imaging condition in which the scalar product of two vector wavefields with source-normalized illumination is exploited to produce scalar images instead of correlating Cartesian components or magnitude of the vector P- and S-wave modes. Compared with alternative methods for correcting the polarity reversal of PS and SP images, our ERTM solution is more stable and simple. Besides these four scalar images, the VB-ERTM method generates another PP-mode image by using the auxiliary stress wavefields. Several 2D and 3D numerical examples are evaluated to demonstrate the potential of our ERTM method.

Sparse domain thresholding filters operating in a sparse domain are highly effective in removing Gaussian random noise under Gaussian distribution assumption. Erratic noise, which designates non-Gaussian noise that consists of large isolated events with known or unknown distribution, also needs to be explicitly taken into account. However, conventional sparse domain thresholding filters based on the least-squares (LS) criterion are severely sensitive to data with high-amplitude and non-Gaussian noise, i.e., the erratic noise, which makes the suppression of this type of noise extremely challenging. In this paper, we present a robust sparsity-promoting denoising model, in which the LS criterion is replaced by the Huber criterion to weaken the effects of erratic noise. The random and erratic noise is distinguished by using a data-adaptive parameter in the presented method, where random noise is described by mean square, while the erratic noise is downweighted through a damped weight. Different from conventional sparse domain thresholding filters, definition of the misfit between noisy data and recovered signal via the Huber criterion results in a nonlinear optimization problem. With the help of theoretical pseudoseismic data, an iterative robust sparsity-promoting filter is proposed to transform the nonlinear optimization problem into a linear LS problem through an iterative procedure. The main advantage of this transformation is that the nonlinear denoising filter can be solved by conventional LS solvers. Tests with several data sets demonstrate that the proposed denoising filter can successfully attenuate the erratic noise without damaging useful signal when compared with conventional denoising approaches based on the LS criterion.

One of the important features of elastic reverse-time migration is that it is a depth domain prestack migration method based on a vectorial wavefield. PP- and PS-wave images can be easily generated by implementing an imaging condition to pure wave modes separated with divergence and curl operators in isotropic media. In the 3D case, however, the curl calculation will generate a 3C vector S-wave, which makes it impossible to generate a scalar PS image with the crosscorrelation imaging condition. Therefore, we first analyzed the polarity distribution of a 3D S-wave and concluded that the separated S-wave keeps perpendicular to a plane determined by the particle motion direction and the propagation direction of the S-wave. This specific plane is the raypath plane when the S-wave is excited by an incident P-wave. The normal direction of the raypath plane can be calculated by computing the vector product of the propagation directions of source and receiver wavefields. Then, we used this normal direction as the reference direction of the S-wave to scalarize the S-wave. Scalarization can be achieved by assigning a positive sign if the vector S-wave is equidirectional with the reference direction or a negative sign if the vector S-wave is contradirectional with the reference direction. Additionally, the scalar S-wave has the same absolute value as the vector S-wave. Next, the P-wave and the scalar S-wave can be applied to an imaging condition to generate a scalar PS image. Numerical examples with true velocity models and those in the presence of velocity error and data noise are presented to demonstrate the potential of the proposed method.

This paper presents a novel approach that aims to predict better reservoir quality regions from seismic inversion and spatial distribution of key reservoir properties from well logs. The reliable estimation of lithology and reservoir parameters at sparsely located wells in the Sawan gas field is still a considerable challenge. This is due to three main reasons: (a) the extreme heterogeneity in the depositional environments, (b) sand-shale intercalations, and (c) repetition of textural changes from fine to coarse sandstone and very coarse sandstone in the reservoir units. In this particular study, machine learning (ML) inversion algorithm was selected to predict the spatial variations of acoustic impedance (AI), porosity, and saturation. While trained in a supervised mode, the support vector machine (SVM) inversion algorithm performed effectively in identifying and mapping individual reservoir properties to delineate and quantify fluid-rich zones. Meanwhile, the Sequential Gaussian Simulation (SGS) and Gaussian Indicator Simulation (GIS) algorithms were employed to determine the spatial variability of lithofacies and porosity from well logs and core analyses data. The calibration of the detailed spatial variations from post-stack seismic inversion using SVM and wireline logs data indicated an appropriate agreement, i.e., variations in AI is related to the variations in reservoir facies and parameters. From the current study, it was concluded that in a highly heterogeneous reservoir, the integration of SVM and GIS algorithms is a reliable approach to achieve the best estimation of the spatial distribution of detailed reservoir characteristics. The results obtained in this study would also be helpful to minimize the uncertainty in drilling, production, and injection in the Sawan gas field of Pakistan as well as other reservoirs worldwide with similar geological settings.