Two-dimensional nonlinear geophysical data filtering using the multidimensional EEMD method

Chih Sung Chen, Yih Jeng

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

A variety of two-dimensional (2D) empirical mode decomposition (EMD) methods have been proposed in the last decade. Furthermore, the multidimensional EMD algorithm and its parallel class, multivariate EMD (MEMD), are available in recent years. From those achievements, it is possible to design an efficient 2D nonlinear filter for geophysical data processing. We introduce a robust 2D nonlinear filter which can be applied to enhance the signal of 2D geophysical data or to highlight the feature component on an image. We did this by replacing the conventionally used smooth interpolation in the ensemble empirical mode decomposition (EEMD) algorithm with a piecewise interpolation method. The one-dimensional (1D) EEMD procedures were consecutively performed in all directions, and then the comparable minimal scale combination technique was applied to the decomposed components. The theoretical derivation, model simulation, and real data applications are demonstrated in this paper. The proposed filtering method is effective in improving the image resolution by suppressing the random noise added in the simulation example and strong low frequency track corrugation noise bands with background noise in the field example. Furthermore, the algorithm can be easily extended to higher dimensions by repeating the same procedure in the succeeding dimension. To evaluate the proposed method, one data set is processed separately by using the enhanced analytic signal method and the multivariate EMD (MEMD) algorithm, and the results from these two methods are compared with that of the proposed method. A general equation for generating three-dimensional (3D) EEMD components based on the comparable minimal scale combination principle is derived for further applications.

Original languageEnglish
Pages (from-to)256-270
Number of pages15
JournalJournal of Applied Geophysics
Volume111
DOIs
Publication statusPublished - 2014 Dec 1

Fingerprint

decomposition
nonlinear filters
interpolation
filter
image resolution
background noise
random noise
method
simulation
derivation
low frequencies

Keywords

  • 2DEMD
  • EEMD
  • Geophysical data processing
  • Multidimensional
  • Multivariate EMD
  • Nonlinear filtering

ASJC Scopus subject areas

  • Geophysics

Cite this

Two-dimensional nonlinear geophysical data filtering using the multidimensional EEMD method. / Chen, Chih Sung; Jeng, Yih.

In: Journal of Applied Geophysics, Vol. 111, 01.12.2014, p. 256-270.

Research output: Contribution to journalArticle

@article{3e85ff15586647de9c9aded8445dfd81,
title = "Two-dimensional nonlinear geophysical data filtering using the multidimensional EEMD method",
abstract = "A variety of two-dimensional (2D) empirical mode decomposition (EMD) methods have been proposed in the last decade. Furthermore, the multidimensional EMD algorithm and its parallel class, multivariate EMD (MEMD), are available in recent years. From those achievements, it is possible to design an efficient 2D nonlinear filter for geophysical data processing. We introduce a robust 2D nonlinear filter which can be applied to enhance the signal of 2D geophysical data or to highlight the feature component on an image. We did this by replacing the conventionally used smooth interpolation in the ensemble empirical mode decomposition (EEMD) algorithm with a piecewise interpolation method. The one-dimensional (1D) EEMD procedures were consecutively performed in all directions, and then the comparable minimal scale combination technique was applied to the decomposed components. The theoretical derivation, model simulation, and real data applications are demonstrated in this paper. The proposed filtering method is effective in improving the image resolution by suppressing the random noise added in the simulation example and strong low frequency track corrugation noise bands with background noise in the field example. Furthermore, the algorithm can be easily extended to higher dimensions by repeating the same procedure in the succeeding dimension. To evaluate the proposed method, one data set is processed separately by using the enhanced analytic signal method and the multivariate EMD (MEMD) algorithm, and the results from these two methods are compared with that of the proposed method. A general equation for generating three-dimensional (3D) EEMD components based on the comparable minimal scale combination principle is derived for further applications.",
keywords = "2DEMD, EEMD, Geophysical data processing, Multidimensional, Multivariate EMD, Nonlinear filtering",
author = "Chen, {Chih Sung} and Yih Jeng",
year = "2014",
month = "12",
day = "1",
doi = "10.1016/j.jappgeo.2014.10.015",
language = "English",
volume = "111",
pages = "256--270",
journal = "Journal of Applied Geophysics",
issn = "0926-9851",
publisher = "Elsevier",

}

TY - JOUR

T1 - Two-dimensional nonlinear geophysical data filtering using the multidimensional EEMD method

AU - Chen, Chih Sung

AU - Jeng, Yih

PY - 2014/12/1

Y1 - 2014/12/1

N2 - A variety of two-dimensional (2D) empirical mode decomposition (EMD) methods have been proposed in the last decade. Furthermore, the multidimensional EMD algorithm and its parallel class, multivariate EMD (MEMD), are available in recent years. From those achievements, it is possible to design an efficient 2D nonlinear filter for geophysical data processing. We introduce a robust 2D nonlinear filter which can be applied to enhance the signal of 2D geophysical data or to highlight the feature component on an image. We did this by replacing the conventionally used smooth interpolation in the ensemble empirical mode decomposition (EEMD) algorithm with a piecewise interpolation method. The one-dimensional (1D) EEMD procedures were consecutively performed in all directions, and then the comparable minimal scale combination technique was applied to the decomposed components. The theoretical derivation, model simulation, and real data applications are demonstrated in this paper. The proposed filtering method is effective in improving the image resolution by suppressing the random noise added in the simulation example and strong low frequency track corrugation noise bands with background noise in the field example. Furthermore, the algorithm can be easily extended to higher dimensions by repeating the same procedure in the succeeding dimension. To evaluate the proposed method, one data set is processed separately by using the enhanced analytic signal method and the multivariate EMD (MEMD) algorithm, and the results from these two methods are compared with that of the proposed method. A general equation for generating three-dimensional (3D) EEMD components based on the comparable minimal scale combination principle is derived for further applications.

AB - A variety of two-dimensional (2D) empirical mode decomposition (EMD) methods have been proposed in the last decade. Furthermore, the multidimensional EMD algorithm and its parallel class, multivariate EMD (MEMD), are available in recent years. From those achievements, it is possible to design an efficient 2D nonlinear filter for geophysical data processing. We introduce a robust 2D nonlinear filter which can be applied to enhance the signal of 2D geophysical data or to highlight the feature component on an image. We did this by replacing the conventionally used smooth interpolation in the ensemble empirical mode decomposition (EEMD) algorithm with a piecewise interpolation method. The one-dimensional (1D) EEMD procedures were consecutively performed in all directions, and then the comparable minimal scale combination technique was applied to the decomposed components. The theoretical derivation, model simulation, and real data applications are demonstrated in this paper. The proposed filtering method is effective in improving the image resolution by suppressing the random noise added in the simulation example and strong low frequency track corrugation noise bands with background noise in the field example. Furthermore, the algorithm can be easily extended to higher dimensions by repeating the same procedure in the succeeding dimension. To evaluate the proposed method, one data set is processed separately by using the enhanced analytic signal method and the multivariate EMD (MEMD) algorithm, and the results from these two methods are compared with that of the proposed method. A general equation for generating three-dimensional (3D) EEMD components based on the comparable minimal scale combination principle is derived for further applications.

KW - 2DEMD

KW - EEMD

KW - Geophysical data processing

KW - Multidimensional

KW - Multivariate EMD

KW - Nonlinear filtering

UR - http://www.scopus.com/inward/record.url?scp=84909587753&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84909587753&partnerID=8YFLogxK

U2 - 10.1016/j.jappgeo.2014.10.015

DO - 10.1016/j.jappgeo.2014.10.015

M3 - Article

AN - SCOPUS:84909587753

VL - 111

SP - 256

EP - 270

JO - Journal of Applied Geophysics

JF - Journal of Applied Geophysics

SN - 0926-9851

ER -