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.