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Acquiring seismic data from irregular topographic surface is oftentlyoppressed by irregular and nonequivalent source-receiver arrays and even moreit yields bad traces after storing the original signal. In the light ofpreprocessing seismic data, we have to extract out most of the given signal,thus further processing and interpretation can obtain extremely accurateoutcomes. We applied Compressed Sensing theorem on Sigmoid vastirregularly-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 ispreserving an optimum signals recovery. The FPOCS seislet transform approachcan achieve accurate and high data recovery results than other methods becauseof a much sparser structure in the seislet transform domain as demonstrated.Moreover, FPOCS algorithm is also efficient in minimizing the number ofrequired iterations to achieve optimum data refilling.

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