Open AccessSeismic trace noise reduction by wavelets and double threshold estimation
Author(s) -
Vargas Regis Nunes,
Paschoarelli Veiga Antônio Cláudio
Publication year - 2017
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
eISSN - 1751-9683
pISSN - 1751-9675
DOI - 10.1049/iet-spr.2017.0061
Subject(s) - wavelet , noise reduction , robustness (evolution) , algorithm , seismic trace , computer science , noise (video) , trace (psycholinguistics) , monte carlo method , reduction (mathematics) , mathematics , pattern recognition (psychology) , statistics , artificial intelligence , image (mathematics) , biochemistry , chemistry , linguistics , philosophy , geometry , gene
In this study, the authors propose the seismic trace noise reduction by wavelets and double threshold estimation method (STNRW), that is based on the discrete wavelet transform, estimates two thresholds instead of the one threshold estimation of the traditional methods. The authors verify the robustness of the method proving that the probability of classification error for a noisy wavelet coefficient decreases, as the length of the signal increases. The authors perform Monte Carlo simulations considering eight seismic traces obtained from astsa R package with different signal‐noise‐to‐ratio (SNR) values in order to compare the performance of the new method with three denoising methods well‐known in the literature. The results show that the STNRW method is efficient.