The Research On Reconstruction Method Of Seismic Data High Resolution Based On Compressive Sensing

In the field seismic exploration,affected by the increasingly complex environmental impact,the seismic data collected directly contains severe noise interference and has extremely low resolution.In severe cases,noise can even affect effective signals.The signal-to-noise ratio,resolution and fidelity of seismic data are important foundations for subsequent accurate interpretation of seismic data to accurately determine the situation of underground reservoirs.Therefore,it is of great significance to study the processing methods of denoising and improving resolution of original seismic data.Compressive sensing theory is a kind of signal processing theory.Using the sparsity of the signal,the signal can recover the original signal containing the complete information from the sampled signal without satisfying the Nyquist sampling theorem.Super-resolution reconstruction is a technology that converts low-resolution signals to high-resolution,which can complement high-frequency details and improve resolution.Based on this,this paper studies the high-resolution reconstruction of seismic data.Higher signal-to-noise ratio is the basis for improving the resolution of seismic data.Therefore,the seismic data denoising based on K-SVD dictionary learning method and morphological component analysis algorithm under the compressive sensing framework is one of the research contents in the paper.The denoising method based on dictionary learning is better than the denoising of seismic data in a single transform domain,which can make up for the shortcoming that the single transform domain cannot be optimally sparse.At the same time,the combination of the morphological component analysis algorithm can decompose the seismic signal into different components,and then adaptively perform dictionary learning,which can realize the optimal sparse representation of the seismic signal,thereby separating the effective signal from the random noise.The high resolution processing of seismic data is another research content of this paper.In this paper,the local self-similarity algorithm in super-resolution reconstruction is applied to compensate high-frequency compensation of seismic signals.At the same time,in order to compensate the sensitivity of the local self-similarity algorithm to pseudo high frequency noise,a high resolution reconstruction method based on compressive sensing is proposed.The main idea is to first denoise seismic data based on K-SVD algorithm and morphological component analysis algorithm,and then reconstruct the local self-similarity algorithm to achieve high-resolution reconstruction of seismic data.This paper verifies this method using model data and actual data.