Factorized Hankel optimal singular spectral approach for erratic and noisy seismic signal denoising

• Optimized and Factorized Hankel (FH) singular methods are presented for noise suppression.
• Optimization enhances colored noise suppression capability in singular spectral methods.
• Factorized Hankel (FH) technique reduces the computational cost.
• The Optimized and FH methods initially tested on synthetic data.
• Compared the results with classical approach on seismic reflection stacked data

De-noising erratic and noisy seismic records is one of the crucial issues in seismic data processing. The presence of colored noise significantly distorts the real signal amplitude leading to misleading results. We present here, a new algorithm based on the Factorized Hankel Optimization method, which is robust in dealing with the above problem. The method involves following two steps: (i) factorization of Hankel matrix and (ii) transformation of trajectory matrix to optimize the noise in singular spectral domain. Initially, we tested the performance of the new algorithm on complex synthetic data and then applied to the real seismic reflection data recorded from the Singareni coalfield, India. The underlying scheme is fast and effective in denoising the complex erratic–noisy seismic signals. We have also compared the robustness of factorization and optimization techniques with respect to conventional singular spectral analysis (SSA). The coal beds and faulted structures identified in the actual seismic reflection data using the new algorithm correlate well with the regional geological structures.