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.
sfkirmigsr implements 2-D Kirchoff prestack depth migration (PSDM).
The following example from tccs/eikods/marm shows an application of sfkirmigsr to imaging synthetic Marmousi data.
With cig= flag, the program can output common-image gathers, as in the followung example from tccs/time2depth2/beinew:
The traveltimes needed for Kirchhoff migration are computed externally and supplied in the form of traveltime tables stable= and rtable=. To increase accuracy, additional information can be provided by traveltime derivatives sderive= and rderiv=, as explained in the paper
Kirchhoff migration using eikonal-based computation of traveltime source-derivatives.
Other useful parameters are antialias= (for controling antialiasing) and aperture= (for controling migration aperture).
The program also has the adjoint flag adj=, which makes it suitable for least-squares inversion.
10 previous programs of the month: