Recompositing of Vast Irregularly-Sampled Seismic Data via Compressed Sensing Framework: An FPOCS Based on Seislet Transform Approach

Abstract

Acquiring seismic data from irregular topographic surface is oftently oppressed by irregular and nonequivalent source-receiver arrays and even more it yields bad traces after storing the original signal. In the light of preprocessing seismic data, we have to extract out most of the given signal, thus further processing and interpretation can obtain extremely accurate outcomes. We applied Compressed Sensing theorem on Sigmoid vast irregularly-sampled seismic data based on the fast projection onto convex sets (FPOCS) algorithm with sparsity constraint in the seislet transform domain, which gives faster convergence than other conventional methods and is preserving an optimum signals recovery. The FPOCS seislet transform approach can achieve accurate and high data recovery results than other methods because of a much sparser structure in the seislet transform domain as demonstrated. Moreover, FPOCS algorithm is also efficient in minimizing the number of required iterations to achieve optimum data refilling.