Reconstruction Of DTCWT Seismic Data Within Compressed Sensing Framework

With the continuous progress of seismic oil exploration technology,the objective environment of the detection area is gradually becoming more complicated,the missing and irregular phenomena of the seismic data images are relatively increasing,which have a negative impact on the subsequent processing and interpretation of seismic data,and add difficulty to the judgement of the location of oil and gas reservoirs.Under the constraints of the Nyquist sampling theorem,the traditional reconstruction method has a relatively high requirement for the sampling rate,and there is not enough sampling and adjustment space in the complex exploration environment,which leads to the huge exploration cost.In order to solve this problem,we need to find a more suitable reconstruction algorithm for seismic data and image,and research it to reduce the cost of seismic exploration process.In this paper,the dual tree complex wavelet transform(DTCWT)is incorporated into the framework of the compressed sensing theory,and the correlation algorithm is proposed to reconstruct the seismic data image and improve the quality of the reconstructed image.The main contents are as follows:1.Research on seismic data reconstruction based on DTCWT shrinkage threshold iterative algorithm.Combined with the theory of compressed sensing,the DWT,DFT,DCT and DTCWT are analyzed,and the seismic data are reconstructed by the above transformation.The experiment shows that the DTCWT is more excellent in the detail expression of the seismic data.According to the experimental results,DTCWT is more suitable for reconstruction of seismic images than those mentioned above.2.Research on dual-tree complex wavelet transform domain dual threshold soft iterative algorithm for seismic image reconstruction.The traditional iterative shrinkage threshold(IST)algorithm does not consider the correlation between different levels of coefficients after sparse transformation,and the effect of reconstruction is relatively limited.By analyzing the correlation between the coefficients and subcoefficients,using the appropriate distribution to fit the wavelet coefficients,establish the double threshold soft iteration.And the experiment of the double threshold soft iteration shows that the quality of reconstructed seismic data is significantly improved compared with the IST reconstruction algorithm.3.Research on seismic data reconstruction based on Bregman iterations with K-SVD dictionary training.This paper uses the split Bregman iterations algorithm which is more suitable for the reconstruction process,and combines the K-SVD dictionary training algorithm to the split Bregman iterations algorithm.From the experimental results,we can see that this algorithm is applied to reconstruct complex seismic data and improve the accuracy of seismic data reconstruction.