SUPPRESSION OF SEISMIC RANDOM NOISE BASED ON STEERABLE FILTERS

Abstract

For seismic random noise suppression, this work designs a steerable filter by taking advantage of elongated Hermite-Gauss functions. According to the different directional responses between valid signal and random noise, we can reconstruct signal by the local characteristics of selected data. With the added directional selectivity, the filtering process can match different direction axes, which makes sure that noise is suppressed without reducing the signal fidelity. The property of directional steerability makes computation more efficient and requires less storage space. Simulation results show that we can get better signal amplitude and denoising effects than traditional wavelet transform and Curvelet transform algorithm by using this method. At –5 db SNR, this method can ensure that the average amplitude reaches 92.99% and SNR enhances 221.774%, which can significantly suppress noise as well as keep the useful signal in processing of real seismic signals.