Reverse-time Migration Imaging Of Sparse Wavefield Compressed Sensing

With the gradual deepening of oil and gas exploration today,the complexity of exploration has increased and the accuracy requirements of imaging have become higher and higher.Based on the advantages of high-precision imaging,the reverse time migration imaging method has become a hot spot in oil and gas exploration.This algorithm mainly solves the problem that conventional migration algorithms cannot achieve fine structural imaging,and provides more accurate information for subsequent structural geological interpretation and oil and gas positioning.The reverse time migration algorithm is based on the characteristics of two-way wave imaging,which can better characterize the characteristics of the seismic wave propagation process and can more realistically reflect the information of the underground medium,but the huge amount of data storage has always been an important factor restricting its development.Compressed sensing,as a sparse recovery theory with breakthrough progress,has been put forward by scholars so far.In the field of seismic exploration,the application of compressed sensing in seismic acquisition and seismic imaging has become a new research direction.This paper mainly studies reverse-time migration imaging of sparse wavefield compressed sensing.In this paper,the wave field data is established based on the first-order velocity stress equation.The second-order time-12 order spatial staggered grid difference method is used for numerical simulation.The split absorption boundary condition is used to eliminate the boundary reflection.To prevent multi-wave imaging crosstalk,vector decomposition is used to mix The wavefield is decomposed into pure wavefields.In order to solve the problem of wave field storage,this paper conducts an in-depth analysis of the sparse representation of data,from algorithm principles to dictionary construction.Then,the orthogonal dictionary and the redundant dictionary are used to conduct sparse comparison test on the seismic wave field data,which is expanded from a single scale to multi-scale analysis,and the data is restored with different sampling rates of different dictionaries to analyze the sparse recovery ability of the data.The sparse test results show that the data has different sparse representations in different dictionaries,and the characteristics of the sparse coefficient are related to the data's own attributes and dictionary attributes.In terms of perceptual data,in view of the difficulty and accuracy of its reconstruction mathematical model,the article focuses on the principles and characteristics of the greedy algorithm,including the basic and improved types.In this paper,three greedy algorithms with different optimization levels are selected to reconstruct the seismic wave field data.Combined with the calculation time and accuracy,the generalized orthogonal matching tracking algorithm is selected as the perception algorithm in this paper.In perceptual imaging,vector inner product imagingconditions are used.To prevent large-angle imaging interference,the seismic wave propagation angle based on Poynting vector and the wave field incidence angle based on velocity component are calculated respectively,and imaging is limited by angle.In view of the noise generated by the migration algorithm,the principle and defects of conventional laplace denoising are discussed,and morphological component analysis is introduced.Based on the sparse representation features of the dictionary,the seismic data morphology of different attributes is separated,and the data amplitude preservation has been achieved.Noise purpose.The final imaging test results show that the algorithm in this paper has a good performance in terms of data volume and accuracy.In this paper,we study the sparse characteristics of the wave field,taking into account data compression and denoising,and achieve breakthroughs in storage and imaging accuracy.