Seismic noise suppression method based on unbalanced depth expectation block logarithm likelihood network

Abstract

The invention discloses a seismic noise suppression method based on an unbalanced depth expectation block logarithm likelihood network, belongs to the technical field of machine learning and seismic image processing, and aims to solve the problem that strong noise cannot be suppressed thoroughly due to the fact that regular term parameters in an expectation block logarithm likelihood algorithm only change along with overall noise variance. The invention provides the seismic noise suppression method based on an unbalanced depth expected block logarithm likelihood network, and the end-to-end denoising network consists of an expected block log-likelihood denoising main network and an unbalanced multilayer perceptron parameter estimation network, and takes a noisy seismic image as an input end. The clean seismic image is used as an output-end learning network parameter. Futhermore the multilayer perception paramter estimation parameter with unbalanced block signal-to-noise ratio is firstlyadopted. According to the method, accurate regular term parameters can be estimated for each block in the seismic image, the denoising intensity of each block can be better controlled, the block denoising effect is improved. The method is superior to a traditional seismic denoising algorithm in the aspects of desert strong noise suppression and signal detail maintenance.

Description

technical field [0001] The invention belongs to the technical field of machine learning and seismic image processing, and in particular relates to a method for suppressing seismic noise based on an unbalanced depth expectation block logarithmic likelihood network. Background technique [0002] Exploration of resources such as oil and natural gas has always been a hot spot for a long time. Seismic exploration is currently the main means of exploration and exploitation of underground energy such as oil and gas. However, due to the influence of the environment of the seismic exploration area, the collected seismic images are often mixed with a large amount of random noise. These noises seriously destroy the effective signal and increase the difficulty of extracting reflected seismic signals. Therefore, seismic random noise suppression is the fundamental problem in seismic data processing to improve seismic quality and extract underground structure information from noise-interfe...

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Solving the Desert Noise Suppression Problem with Unequalized Deep Desired Block Log-Likelihood Networks

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