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

TL;DR

This paper introduces a novel seismic data reconstruction method using compressed sensing with FPOCS and seislet transform, enabling faster and more accurate recovery of irregularly-sampled seismic signals.

Contribution

It presents a new seismic data reconstruction framework combining FPOCS with seislet transform for improved speed and accuracy over existing methods.

Findings

FPOCS seislet approach achieves higher data recovery accuracy.

The method converges faster than traditional techniques.

It effectively reconstructs signals from irregularly-sampled seismic data.

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.