Seismic Data Denoising Analysis Based on Monte Carlo Block Theory

Abstract Denoising of seismic data has always been an important focus in the field exploration, which is very for processing and interpretation data. With increasing complexity exploration environment target, containing strong noise weak amplitude in-phase axis often contain many feature signals. However, phase characteristics are highly susceptible to useful signal submerged by background noise, seriously affected precision interpretation, dictionary based on theory monte carlo study denoising method, selecting expect more blocks data, accurate MOD dictionary, gain a higher quality Monte block this paper, learning rules, random example analysis test, algorithm results three methods deal with, numerical show that better ability, have SNR, effectively keep data; In terms computational efficiency, proposed method requires less time thus verifying feasibility effectiveness method.