Research on Efficient Seismic Data Acquisition Methods Based on Sparsity Constraint

In actual exploration,the demand for 3D seismic data collection is increasing,and the requirements for data are becoming higher and higher.Accordingly,the collection cost and data volume also increase.Aiming at this problem,we make use of the nature of data sparse expression,based on the theory of compressed sensing,to carry out the research on the efficient collection method of seismic data.It combines the collection of seismic data and the compression in data processing in practical work,breaking through the limitation of the traditional sampling frequency,and the sparse characteristics of the seismic signal are utilized to reconstruct the missing data.We focus on the key elements of the sampling matrix in the theory of compressed sensing,and study the methods of seismic data acquisition.According to the conditions that the compressed sensing sampling matrix needs to meet,we introduce a new random acquisition scheme,which introduces the widely used Low-density Parity-check(LDPC)sampling matrix in image processing into seismic exploration acquisition.Firstly,its properties are discussed and its conditions for satisfying the sampling matrix in compressed sensing are verified.Then the LDPC sampling method and the conventional data acquisition method are used to synthesize seismic data reconstruction experiments.The reconstruction results,signal-to-noise ratio and reconstruction error are compared to verify the seismic data based on sparse constraints.The LDPC sampling method improves the current seismic data reconstruction efficiency,reduces the exploration cost and the effectiveness and feasibility of the method.

Based on Compressed Sensing of Orthogonal Matching Pursuit Algorithm Image Recovery

Compressive sensing theory mainly includes the sparsely of signal processing,the structure of the measurement matrix and reconstruction algorithm.Reconstruction algorithm is the core content of CS theory,that is,through the low dimensional sparse signal recovers the original signal accurately.This thesis based on the theory of CS to study further on seismic data reconstruction algorithm.We select orthogonal matching pursuit algorithm as a base reconstruction algorithm.Then do the specific research for the implementation principle,the structure of the algorithm of AOMP and make the signal simulation at the same time.In view of the OMP algorithm reconstruction speed is slow and the problems need to be a given number of iterations,which developed an improved scheme.We combine the optimized OMP algorithm of constraint the optimal matching of item selection strategy,the backwards gradient projection ideas of adaptive variance step gradient projection method and the original algorithm to improve it.Simulation experiments show that improved OMP algorithm is superior to traditional OMP algorithm of improvement in the reconstruction time and effect under the same condition.This paper introduces CS and most mature compressive sensing algorithm at present orthogonal matching pursuit algorithm.Through the program design realize basic orthogonal matching pursuit algorithms,and design realize basic orthogonal matching pursuit algorithm of one-dimensional,two-dimensional signal processing simulation.

Image Reconstruction Based on Compressed Sensing Measurement Matrix Optimization Method

In this paper,the observation matrix and reconstruction algorithm of compressed sensing sampling theorem are studied.The advantages and disadvantages of greedy reconstruction algorithm are analyzed.The disadvantages of signal sparsely are preset in this algorithm.The sparsely adaptive estimation algorithm is proposed.The compressed sampling matching tracking algorithm supports the set selection and culling atomic standards to improve.The sparse step size adaptive compressed sampling matching tracking algorithm is proposed.The improved algorithm selects the sparsely as the step size to select the support set atom,and the maximum correlation value.Half of the threshold culling algorithm supports the concentration of excess atoms.The experimental results show that the improved algorithm has better power and lower image reconstruction error under the same sparsely criterion,and has higher image reconstruction quality and visual effects.