Multiscale Sparse Dictionary Learning With Rate Constraint for Seismic Data Compression

Seismic survey is one of the most effective tools for oil and gas exploration. To date, there has been an exponential growth in the size of seismic data required for large-scale seismic survey. For transmission and storage purposes, we propose a novel seismic compression method. First, a multiscale sparse dictionary learning model with rate constraint is presented. By combining the advantages of multiscale decomposition and dictionary learning, the seismic data could be effectively represented as a sparse matrix. Rate constraints are used to obtain the sparse coefficients that are properly tailored to the compression objective. To solve the optimization problem, the alternating direction method of multipliers is adopted. Furthermore, a seismic compression scheme based on the learned dictionary is introduced. Finally, public seismic datasets are used to verify the efficiency of different seismic data compression methods. The experimental results indicate that the proposed method achieves the best seismic compression performance, including rate-distortion tradeoff and visual quality.