Seismic model estimation using particle swarm optimization

Modern seismic data surveys generate terabytes of data daily leading to a significant increase of the cost for storage and transmission. Therefore, it is desired to compress seismic data. In this work, we propose a model-based compression scheme to deal with the large data volume. First, each seismic trace is modeled as a superposition of multiple exponentially decaying sinusoidal waves (EDSWs). Each EDSW represents a model component and is defined by a set of parameters. Secondly, a parameter estimation algorithm for this model is proposed using Particle Swarm Optimization (PSO) technique. In the proposed algorithm, the parameters of each EDSW are estimated sequentially wave by wave. A suitable number of model components for each trace is determined according to the level of the residuals energy. The proposed model based compression scheme is then experimentally compared with the discrete Cosine transform (DCT) on a real seismic data. The proposed model based algorithm outperforms the DCT in term of compression ratio and reconstruction quality.