Complex noise suppression and reconstruction of seismic reflection data from fault structures using Space Lagged Singular Spectral Analysis

Abstract. The seismic reflection data processing to identify thin coal beds and intrinsic fault structure associated with coalmines suffers from the coherent noise that arises due to interference and diffraction of seismic signals from adjacent horizontal boundaries on either sides of the fault structure. The amplitudes of the interfering reflections mislead the interpretation of geological features like faults, curved reflectors, etc. In particular, correlated and erratic noise create more severe problem than the random noise in the interpretation of such complex geological structures. Here, we employed Space Lagged Singular Spectral Analysis (SLSSA) algorithm, which decomposes the amplitudes from a constant time/depth to determine the original signal amplitude based on eigen properties of the signal. Thus, we can de-noise seismic signal to delineate the concealed discontinuities and to map the fault structures. Initially, we tested the algorithm on the synthetic data of fault structure embedded with complex mixed noise (random and colored) of known percentage. Finally, the method was employed on high-resolution seismic reflection observations recorded from Singareni coalfield, India. The SLSSA method reveals some significant kinematic fault structures in the coal-bearing zone, which agreed with regional fault structures in the PG basin and correlates well with available geological information in the area.