Seismic data resolution improvement based on compressed sensing

Abstract

We introduce the theory of compressed sensing into seismic data processing in this paper and discus a method for seismic data resolution improvement based on compressed sensing theory. We also put forward some measures to improve signal sparsity, observation matrix design, signal reconstruction and so on. Since Gauss random matrix has instability of calculation results, we use larger reflection-constraints matrix based on seismic signal characteristics, which can enhance reconstruction algorithm speed and accuracy. In order to suppress locally and regionally homogenized noise and enhance weak signals, we review seismic data normalization and put forward a method of joint regional and local compression sensing. Experimental results of model and real data show that the proposed method can extract weak signals and improve seismic resolution.