Seismic Data Reconstruction with Shearlet Operators based on Compressed Sensing

Under the framework of compressed sensing theory, the seismic data reconstruction technology based on Shearlet sparse representation is studied. It consists of three parts: construction, solution and quantitative evaluation of seismic data reconstruction algorithm. First, seismic data reconstruction algorithm based on compressed sensing theory using the Shearlet sparse transform has been built; secondly, fast projection onto convex set (FPOCS) algorithm with high convergence and accuracy has been researched and index model has been chosen as the threshold strategy to obtain the optimal solution; finally, quantitative evaluation has been done to the data reconstruction results using two parameters. Testing results verify the correctness and effectiveness of this method, and its reconstruction accuracy is higher than that of Fourier and Curvelet sparse transform.