  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.
