A method and device for separating aliasing data based on sparse inversion

Abstract

The invention provides a method and a device for separating aliasing data based on sparse inversion. The method comprises the following steps: splitting aliasing seismic data to obtain 3D common-geophone gather data; constructing a threshold contraction function according to the 3D common-geophone gather data, wherein the threshold contraction function includes the relation between the number of iterations and the amplitude range of the seismic data; constructing a sparse constrained objective function according to the aliasing seismic data, wherein the sparse constrained objective function includes the function relation between the aliasing seismic data and effective signal data; and using a constructed 3D sliding space-time window to iteratively invert and solve the sparse constrained objective function according to the threshold contraction function to obtain signal-noise separation data of the aliasing seismic data. By using the embodiments of the application, the separation effectof seismic data is improved, and the efficiency of data processing is improved.

Description

technical field [0001] The application belongs to the technical field of seismic data processing, and in particular relates to a method and device for separating aliasing data based on sparse inversion. Background technique [0002] Due to the low efficiency and high cost of traditional seismic acquisition methods, they cannot meet the industrial needs of high-density seismic data acquisition. In the existing technology, aliasing acquisition technology can be used to improve the daily efficiency of seismic acquisition and reduce the cost of high-density seismic acquisition. However, since the aliasing acquisition technology adopts the continuous excitation method, there are a large amount of adjacent shot interference noise in the aliasing acquisition data, which seriously reduces the signal-to-noise ratio and imaging quality of seismic data. Therefore, the signal-to-noise separation of aliased data is a necessary link in the processing of aliased collected data. [0003] I...

AI Technical Summary

Problems solved by technology

The direct denoising method and iterative denoising method mainly use the random characteristics of the adjacent shot interference on the non-shot set to suppress the noise. Their disadvantage is that when the seismic data has a high degree of aliasing, these methods usually lead to serious noise residuals and Signal damage, aliasing data separation effect is not good, it is difficult to meet the actual production needs

The inversion separation method is based on the coherent characteristics of the signal, imposes sparse constraints on the seismic data in the transform domain to extract effective signals, and uses the predictability between the signal and noise to achieve signal-to-noise separation. The sparsity and the threshold shrinkage function adopted, the calculation cost is high, and the separation effect of aliased data is not good

Many inversion separation methods currently proposed have deficiencies. For example, the separation methods based on FK (Frequency-Wavenumber) transform and linear Radon (Radon transform) transform are not sparse in the representation of curved events in the transform domain, which affects aliasing acquisition. data separation effect

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