Joint constraint random noise suppression method based on sparse regularization

Abstract

The invention relates to a joint constraint random noise suppression method based on sparse regularization, and belongs to the technical field of geophysical exploration. The method specifically comprises the following steps: S1, inputting an original seismic record, and constructing an objective function of curvelet transform-second-order generalized total variation joint constraint denoising according to sparse features of noisy data in a curvelet domain and an image gradient domain; S2, converting the L1-L2 norm regularization model containing the curvelet transform constraint term into a standard basis pursuit noise reduction problem, and inverting a curvelet coefficient with the minimum L1 norm to obtain a seismic record after preliminary denoising; and S3, taking the preliminarily denoised seismic record as an input image, solving a denoising problem of the second-order generalized total variation constraint to realize random noise suppression of the joint constraint, and finallyoutputting seismic data with an enhanced signal-to-noise ratio. According to the method, the denoising effect on random noise in the seismic data is improved, and weak signal energy can be effectively protected, so that high-quality processing of subsequent seismic data and the reliability of a seismic geological interpretation result are guaranteed.

Description

technical field [0001] The invention belongs to the technical field of geophysical exploration, and specifically relates to a denoising method based on joint constraints of sparse regularization, which is applied to the suppression of random noise contained in actual seismic data and the protection of weak effective signal energy. Background technique [0002] Seismic exploration is one of the routine means of detecting natural resources such as oil, gas and minerals. However, limited by factors such as complex acquisition environment and acquisition technology, seismic data obtained in the field are often accompanied by noise and missing seismic traces, and in the process of seismic data processing, there will also be artificial artifacts caused by unsuitable processing methods. Affect the final seismic data imaging, and even mislead the seismic geological interpretation. [0003] The original acquired seismic data contains two types of regular noise and random noise. Exi...

AI Technical Summary

Problems solved by technology

However, when simply using the second-order generalized total variation regularized constrained random noise suppression method, if the noise is over suppressed, a large amount of important information in the image will be lost. On the contrary, if the detailed information of the seismic data is to be retained, it may be difficult to suppress the noise Effective suppression, leading to poor denoising effect

Images