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.

Obtaining the bursting liability is essential for rock burst mitigation. To reasonably evaluate the bursting liability of coal in deep coal mining, a new modified bursting energy index considering rebound and damage effects of rock is proposed. Then, uniaxial compressive tests of gypsum–rock combination specimens with different rock types are conducted for researching the energy evolution and bursting liability. Third, the traditional bursting energy index K_E and modified bursting energy index K_ED are compared. At last, the reasonability of K_ED is verified through PFC2D simulation. Results show: (1) When evaluating the bursting liability of coal–rock combination, the rebound and damage effects of rock must be considered. (2) The uniaxial compressive strength of gypsum–rock combination specimen nonlinearly increases with increasing rock strength, but the dynamic failure duration exponentially decreases. (3) K_E and K_ED all increase linearly when the rock strength increases. (4) The slope of fitting curve of K_ED is smaller than that of K_E, indicating that the bursting liability calculated by K_E is strengthened. (5) PFC2D simulation shows that K_ED decreases with increasing number of drilling hole in the coal component. When using K_ED to evaluate the bursting liability of coal–rock combination, it is more reasonable and applicable.

Discoveries of heavy crude oil in the Neoproterozoic rocks (Infracambrian rock sequence) from the Bikaner-Nagaur Basin of India emphasize the significance of studying and exploring the Neoproterozoic source rock potential in the southeastern part of Pakistan. This study evaluates the potential of the source rock in the Infracambrian rock sequence (Salt Range Formation) based on surface geochemical surveys, Rock–Eval pyrolysis, source biomarkers, geophysical characterization, and seismic inversion using machine learning for maturity index estimation. Core samples of Infracambrian rock were extracted for Rock–Eval pyrolysis and biomarker characterization. Additionally, 81 geomicrobial soil and gas samples were collected from the surface to explore the petroleum system and potential source rocks in the subsurface. Advanced interpretation techniques were used to investigate the origin and concentration of hydrocarbon gases at the surface, including Rock–Eval pyrolysis, thermal maturity, source biomarkers, and the environment of deposition of organic matter. The results show that the investigated samples are characterized by restricted marine clay devoid of sedimentary carbonate facies with thermal maturity in the early stage of the oil generation window. The seismic inverted maturity index profile demonstrates a reasonable correlation of thermal maturity with the biomarkers and Rock–Eval pyrolysis. Further scrutiny of the surface geochemical samples confirms the presence of higher concentrations of thermogenic C₂–C₄ hydrocarbons in the vicinity of anticlinal structures, suggesting the existence of an effective migration path along deep-seated faults to the surface. This study concludes that the Infracambrian rocks on the eastern flank of Pakistan are thicker, thermally mature, and have deep-seated structural closures, indicating a greater probability of heavy and light oil in this area than in the Bikaner–Nagaur Basin, India.

Simultaneous source acquisition has attracted more and more attention from geophysicists because of its cost savings, whereas it also brings some challenges that have never been addressed before. Deblending of simultaneous source data is usually considered as an underdetermined inverse problem, which can be effectively solved with a least-squares (LS) iterative procedure between data consistency (ℓ2-norm) and regularization (ℓ0-norm or ℓ1-norm). However, when it comes to abnormal noise that follows non-Gaussian distribution and possesses high-amplitude features (e.g., erratic noise, swell noise, and power line noise), the ℓ2-norm is a nonrobust statistic that can easily lead to suboptimal deblended results. Although abnormal noise can be attenuated in the common source domain at first, it is still challenging to apply a coherency-based filter due to the sparse receiver or crossline sampling, e.g., that commonly found in ocean bottom node (OBN) acquisition. To address this problem, we have developed a normalized shaping regularization to make the inversion-based deblending approach robust for the separation of blended data when abnormal noise exists. Its robustness comes from the normalized shaping operator defined by the confidence interval of normal distribution, which minimizes the abnormal risk to a normal level to satisfy the assumption of LS shaping regularization. In special cases, the proposed approach will revert to the classic LS shaping regularization once the normalized coefficient is large enough. Experimental results on synthetic and field data indicate that the proposed method can effectively restore the separated records from blended data at essentially the same convergence rate as the LS shaping regularization for the abnormal noise-free scenario, but it can obtain better deblending performance and less energy leakage when abnormal noise exists.

Three-dimensional elastic reverse time migration has been confronted with the problem of generating scalar images with vector S-waves. The underlying principle for solving this problem is to convert the vector S-waves into scalars. Previous methods were mainly focused on PS-imaging, but they usually cannot work properly on SP- and SS-cases. The complexity of SP- and SS-imaging arises from the fact that the incident S-wave has unpredictable relationship with the raypath plane. We have suggested that S-wave should be treated separately as SV- and SH-waves, which keep predictable relationships with the raypath plane. First, the elastic wavefield is separated into P- and S-waves using the Helmholtz decomposition. Then, we evaluate the normal direction of the raypath plane at each imaging grid. Next, we separate the vector S-wave obtained with curl operator into SH- and SV-waves, both of which are scalars. Finally, correlation imaging conditions are implemented to those scalar wave modes to produce scalar SV-P, SV-SV, and SH-SH images.