A simple method inspired by empirical mode decomposition for denoising seismic data

We developed a new and simple method for denoising seismic data, which was inspired by data-driven empirical mode decomposition (EMD) algorithms. The method, which can be applied either as a trace-by-trace process or in the f-x domain, replaces the use of the cubic interpolation scheme, which is req...

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Detalles Bibliográficos
Autores principales: Gómez, Julián Luis, Velis, Danilo Rubén
Formato: Articulo Preprint
Lenguaje:Inglés
Publicado: 2015
Materias:
Geofísica
Signal processing
Algorithm
Noise
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/99274
https://ri.conicet.gov.ar/11336/54882
https://library.seg.org/doi/10.1190/geo2015-0566.1
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-99274
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Geofísica
Signal processing
Algorithm
Noise
spellingShingle Geofísica
Signal processing
Algorithm
Noise
Gómez, Julián Luis
Velis, Danilo Rubén
A simple method inspired by empirical mode decomposition for denoising seismic data
topic_facet Geofísica
Signal processing
Algorithm
Noise
description We developed a new and simple method for denoising seismic data, which was inspired by data-driven empirical mode decomposition (EMD) algorithms. The method, which can be applied either as a trace-by-trace process or in the f-x domain, replaces the use of the cubic interpolation scheme, which is required to calculate the mean envelopes of the signal and the residues, by window averaging. The resulting strategy is not viewed as an EMD per se, but a userfriendly version of EMD-based algorithms that permits us to attain, in a fraction of the time, the same level of noise cancellation as standard EMD implementations. Furthermore, the proposed method requires less user intervention and easily processes millions of traces in minutes rather than in hours as required by conventional EMD-based techniques on a standard PC. We compared the performance of the new method against standard EMD methods in terms of computational cost and signal preservation and applied them to denoise synthetic and field (microseismic and poststack) data containing random, erratic, and coherent noise. The corresponding f-x EMDs implementations for lateral continuity enhancement were analyzed and compared against the classical f-x deconvolution to test the method.
format Articulo
Preprint
author Gómez, Julián Luis
Velis, Danilo Rubén
author_facet Gómez, Julián Luis
Velis, Danilo Rubén
author_sort Gómez, Julián Luis
title A simple method inspired by empirical mode decomposition for denoising seismic data
title_short A simple method inspired by empirical mode decomposition for denoising seismic data
title_full A simple method inspired by empirical mode decomposition for denoising seismic data
title_fullStr A simple method inspired by empirical mode decomposition for denoising seismic data
title_full_unstemmed A simple method inspired by empirical mode decomposition for denoising seismic data
title_sort simple method inspired by empirical mode decomposition for denoising seismic data
publishDate 2015
url http://sedici.unlp.edu.ar/handle/10915/99274
https://ri.conicet.gov.ar/11336/54882
https://library.seg.org/doi/10.1190/geo2015-0566.1
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