An old paper is added to the collection of reproducible documents: High-resolution recursive stacking using plane-wave construction

We propose an approach to normal moveout (NMO) stacking, which eliminates the effects of “NMO stretch” and restores a wider frequency band by replacing conventional stacking with a regularized inversion to zero offset. The resulting stack is a model that best fits the data using additional constraints imposed by shaping regularization. We introduce a recursive stacking scheme using plane-wave construction in the backward operator of shaping regularization to achieve a higher resolution stack. The advantage of using recursive stacking along local slopes in the application to NMO and stack is that it avoids “stretching effects” caused by NMO correction and is insensitive to non-hyperbolic moveout in the data. Numerical tests demonstrate the algorithm’s ability to attain a higher frequency stack with a denser temporal sampling interval compared to those of the conventional stack and to minimize stretching effects caused by NMO correction. We apply this method to a 2-D field dataset from the North Sea and achieve noticeable resolution improvements in the stacked section compared with that of conventional NMO and stack.

An old paper is added to the collection of reproducible documents: Relative time seislet transform

The seislet transform utilizes the wavelet-lifting scheme and local slopes to analyze the seismic data. In its definition, the designing of prediction operators specifically for seismic images and data is an important issue. We propose a new formulation of the seislet transform based on the relative time (RT) attribute. This method uses RT volume to construct multiscale prediction operators. With the new prediction operators, the seislet transform gets accelerated since distant traces get predicted directly. We apply the proposed method to synthetic and real data to demonstrate that the new approach reduces computational cost and obtains excellent sparse representation on test datasets.

An old paper is added to the collection of reproducible documents: Probabilistic moveout analysis by time warping

Parameter estimation from reflection moveout analysis represents one of the most fundamental problems in subsurface model building. We propose an efficient moveout inversion method that is based on the process of automatic flattening of common-midpoint (CMP) gathers using local slopes. We show that as a byproduct of this flattening process, we can also estimate reflection traveltimes corresponding to the flattened CMP gathers. This traveltime information allows us to construct a highly overdetermined system and subsequently invert for moveout parameters including normal-moveout (NMO) velocities and quartic coefficients related to anisotropy. We utilize the 3D generalized moveout approximation (GMA) that can accurately capture the effects of complex anisotropy on reflection traveltimes as the basis for our moveout inversion. Due to the cheap forward traveltime computations by GMA, we employ a Monte Carlo inversion scheme for an improved handling of the non-linearity between reflection traveltimes and moveout parameters. This choice also allows us to set up a probabilistic inversion workflow within a Bayesian framework, where we can obtain the posterior probability distributions that contain valuable statistical information on estimated parameters such as uncertainty and correlations. We use synthetic and real-data examples including the data from the SEAM Phase II unconventional reservoir model to demonstrate the performance of our proposed method and discuss insights into the problem of moveout inversion gained from analyzing the posterior probability distributions. Our results suggest that the solutions to the problem of traveltime-only moveout inversion from 2D CMP gathers are relatively well-constrained by the data. However, parameter estimation from 3D CMP gathers associated with more moveout parameters and complex anisotropic models are generally non-unique and that there are trade-offs among inverted parameters, especially the quartic coefficients.

An old paper is added to the collection of reproducible documents: Multichannel adaptive deconvolution based on streaming prediction-error filter

Deconvolution mainly improves the resolution of seismic data by compressing seismic wavelets, which is of great significance in high-resolution processing of seismic data. Prediction-error filtering/least-square inverse filtering is widely used in seismic deconvolution and usually assumes that seismic data is stationary. Affected by factors such as earth filtering, actual seismic wavelets are time- and space-varying. Adaptive prediction-error filters are designed to effectively characterize the nonstationarity of seismic data by using iterative methods, however, it leads to problems such as slow calculation speed and high memory cost when dealing with large-scale data. We have proposed an adaptive deconvolution method based on a streaming prediction-error filter. Instead of using slow iterations, mathematical underdetermined problems with the new local smoothness constraints are analytically solved to predict time-varying seismic wavelets. To avoid the discontinuity of deconvolution results along the space axis, both time and space constraints are used to implement multichannel adaptive deconvolution. Meanwhile, we define the parameter of the time-varying prediction step that keeps the relative amplitude relationship among different reflections. The new deconvolution improves the resolution along the time direction while reducing the computational costs by a streaming computation, which is suitable for handling nonstationary large-scale data. Synthetic model and filed data tests show that the proposed method can effectively improve the resolution of nonstationary seismic data, while maintaining the lateral continuity of seismic events. Furthermore, the relative amplitude relationship of different reflections is reasonably preserved.

An old paper is added to the collection of reproducible documents: Continuous time-varying Q-factor estimation method in the time-frequency domain

The Q-factor is an important physical parameter for characterizing the absorption and attenuation of seismic waves propagating in underground media, which is of great significance for improving the resolution of seismic data, oil and gas detection, and reservoir description. In this paper, the local centroid frequency is defined using shaping regularization and used to estimate the Q values of the formation. We propose a continuous time-varying Q-estimation method in the time-frequency domain according to the local centroid frequency, namely, the local centroid frequency shift (LCFS) method. This method can reasonably reduce the calculation error caused by the low accuracy of the time picking of the target formation in the traditional methods. The theoretical and real seismic data processing results show that the time-varying Q values can be accurately estimated using the LCFS method. Compared with the traditional Q-estimation methods, this method does not need to extract the top and bottom interfaces of the target formation; it can also obtain relatively reasonable Q values when there is no effective frequency spectrum information. Simultaneously, a reasonable inverse Q filtering result can be obtained using the continuous time-varying Q values.