Wavelet tight frame adaptive sparse three-dimensional seismic data reconstruction method

Abstract

The invention discloses a wavelet tight frame adaptive sparse three-dimensional seismic data reconstruction method, which is applied to the field of seismic data processing, and aims at the limitationof traditional fixed base sparse transformation and the problem that an existing adaptive sparse transformation method is insufficient in stability. Through implementing the wavelet tight frame adaptive sparse three-dimensional seismic data reconstruction method, the stability of dictionary learning is improved by using a wavelet tight frame, the optimal sparse representation of data is obtainedthrough adopting a stable dictionary learning method, and the reconstruction of the three-dimensional seismic data of a complex structure is realized and the noise reduction effect is good by using the perfect reconstruction characteristic of the wavelet tight frame; and by using the seismic data reconstructed by adopting the wavelet tight frame adaptive sparse three-dimensional seismic data reconstruction method, the reliability of the underground information obtained by three-dimensional seismic exploration is improved, more accurate underground information is provided for the next production work, and the purposes of improving the production quality and reducing the economic cost are achieved.

Description

technical field [0001] The invention belongs to the field of seismic data processing, in particular to a seismic data reconstruction technology. Background technique [0002] 3D seismic exploration is the main technical method of oil and gas exploration, which is mainly divided into three steps: acquisition, processing and interpretation of seismic data. The purpose of seismic exploration is to obtain reliable underground information, but it needs to be supported by high-precision seismic data. In the process of actual seismic data acquisition, due to factors such as wind and grass, transportation, etc., the acquired data generally contain noise, and due to economic cost control, terrain and surface obstacles and other factors, the acquired data may also have missing seismic traces . Such data will have a serious impact on subsequent processing and interpretation work, so reconstruction of high-precision 3D seismic data is an important issue. [0003] There are many types...

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Problems solved by technology

[0005] The above methods do not focus on the problem of the degree of freedom of the dictionary, resulting in poor stability of dictionary learning

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