Optimization of sub-band coding method for seismic data compression

Seismic data volumes, which require huge transmission capacities and massive storage media, continue to increase rapidly due to acquisition of 3D and 4D multiple streamer surveys, multicomponent data sets, reprocessing of prestack seismic data, calculation of post‐stack seismic data attributes, etc. We consider lossy compression as an important tool for efficient handling of large seismic data sets. We present a 2D lossy seismic data compression algorithm, based on sub‐band coding, and we focus on adaptation and optimization of the method for common‐offset gathers. The sub‐band coding algorithm consists of five stages: first, a preprocessing phase using an automatic gain control to decrease the non‐stationary behaviour of seismic data; second, a decorrelation stage using a uniform analysis filter bank to concentrate the energy of seismic data into a minimum number of sub‐bands; third, an iterative classification algorithm, based on an estimation of variances of blocks of sub‐band samples, to classify the sub‐band samples into a fixed number of classes with approximately the same statistics; fourth, a quantization step using a uniform scalar quantizer, which gives an approximation of the sub‐band samples to allow for high compression ratios; and fifth, an entropy coding stage using a fixed number of arithmetic encoders matched to the corresponding statistics of the classified and quantized sub‐band samples to achieve compression. Decompression basically performs the opposite operations in reverse order. We compare the proposed algorithm with three other seismic data compression algorithms. The high performance of our optimized sub‐band coding method is supported by objective and subjective results.