Adaptive filtering of random noise in near-surface seismic and ground-penetrating radar data

Yih Jeng, Yi Wei Li, Chih Sung Chen, Hsin Yi Chien

Research output: Contribution to journalArticle

24 Citations (Scopus)

Abstract

Adaptive filtering is an effective method to suppress speckle noise in 2D digital image data. Recently, a variety of adaptive filtering algorithms have been developed and employed to remove random noise from geophysical data. In this paper, two filters are designed by adopting adaptive algorithms, the optimum 2D median filter, (a 2D median filter with an optimum window size), and the 2D adaptive Wiener filter (a real time optimal filter renovated from the conventional Wiener filter technology) to investigate the advantages of using adaptive filters in processing ultra-shallow seismic and ground-penetrating radar data. Synthetic common-shot record with added white Gaussian noise was employed to test the effects of 2D window size on both filtering processes. To demonstrate the practical performance of the filter, we processed a set of prestack ultra-shallow seismic data recorded from a shallow fault zone and a stacked section of ground-penetrating radar data as real examples. Examining the performances of the two filters both in time and frequency domains, we notice that the recovery of the original signal depends on the attribute, intensity, and density of the noise. Inspecting the filtered synthetic records in t-x domain, these two filters not only successfully remove the random noise but also suppress the ground roll. With the prestack seismic field data, the median filter renders better resolution than the Wiener filter, but it also suppresses signals that may have geological implications, making the result less desirable. In addition, both the adaptive filters improve the geologically interesting low frequency components of the stacked ground-penetrating radar data, but the high frequency components are blurred.

Original languageEnglish
Pages (from-to)36-46
Number of pages11
JournalJournal of Applied Geophysics
Volume68
Issue number1
DOIs
Publication statusPublished - 2009 May 1

Fingerprint

ground penetrating radar
radar data
random noise
filter
filters
adaptive filters
speckle
shot
digital image
recovery
fault zone
seismic data
low frequencies

Keywords

  • 2D median filter
  • Adaptive Wiener filter
  • Adaptive filtering
  • GPR
  • Seismic

ASJC Scopus subject areas

  • Geophysics

Cite this

Adaptive filtering of random noise in near-surface seismic and ground-penetrating radar data. / Jeng, Yih; Li, Yi Wei; Chen, Chih Sung; Chien, Hsin Yi.

In: Journal of Applied Geophysics, Vol. 68, No. 1, 01.05.2009, p. 36-46.

Research output: Contribution to journalArticle

Jeng, Yih ; Li, Yi Wei ; Chen, Chih Sung ; Chien, Hsin Yi. / Adaptive filtering of random noise in near-surface seismic and ground-penetrating radar data. In: Journal of Applied Geophysics. 2009 ; Vol. 68, No. 1. pp. 36-46.
@article{654f68f9a8414136b2207e4b2020c5e6,
title = "Adaptive filtering of random noise in near-surface seismic and ground-penetrating radar data",
abstract = "Adaptive filtering is an effective method to suppress speckle noise in 2D digital image data. Recently, a variety of adaptive filtering algorithms have been developed and employed to remove random noise from geophysical data. In this paper, two filters are designed by adopting adaptive algorithms, the optimum 2D median filter, (a 2D median filter with an optimum window size), and the 2D adaptive Wiener filter (a real time optimal filter renovated from the conventional Wiener filter technology) to investigate the advantages of using adaptive filters in processing ultra-shallow seismic and ground-penetrating radar data. Synthetic common-shot record with added white Gaussian noise was employed to test the effects of 2D window size on both filtering processes. To demonstrate the practical performance of the filter, we processed a set of prestack ultra-shallow seismic data recorded from a shallow fault zone and a stacked section of ground-penetrating radar data as real examples. Examining the performances of the two filters both in time and frequency domains, we notice that the recovery of the original signal depends on the attribute, intensity, and density of the noise. Inspecting the filtered synthetic records in t-x domain, these two filters not only successfully remove the random noise but also suppress the ground roll. With the prestack seismic field data, the median filter renders better resolution than the Wiener filter, but it also suppresses signals that may have geological implications, making the result less desirable. In addition, both the adaptive filters improve the geologically interesting low frequency components of the stacked ground-penetrating radar data, but the high frequency components are blurred.",
keywords = "2D median filter, Adaptive Wiener filter, Adaptive filtering, GPR, Seismic",
author = "Yih Jeng and Li, {Yi Wei} and Chen, {Chih Sung} and Chien, {Hsin Yi}",
year = "2009",
month = "5",
day = "1",
doi = "10.1016/j.jappgeo.2008.08.013",
language = "English",
volume = "68",
pages = "36--46",
journal = "Journal of Applied Geophysics",
issn = "0926-9851",
publisher = "Elsevier",
number = "1",

}

TY - JOUR

T1 - Adaptive filtering of random noise in near-surface seismic and ground-penetrating radar data

AU - Jeng, Yih

AU - Li, Yi Wei

AU - Chen, Chih Sung

AU - Chien, Hsin Yi

PY - 2009/5/1

Y1 - 2009/5/1

N2 - Adaptive filtering is an effective method to suppress speckle noise in 2D digital image data. Recently, a variety of adaptive filtering algorithms have been developed and employed to remove random noise from geophysical data. In this paper, two filters are designed by adopting adaptive algorithms, the optimum 2D median filter, (a 2D median filter with an optimum window size), and the 2D adaptive Wiener filter (a real time optimal filter renovated from the conventional Wiener filter technology) to investigate the advantages of using adaptive filters in processing ultra-shallow seismic and ground-penetrating radar data. Synthetic common-shot record with added white Gaussian noise was employed to test the effects of 2D window size on both filtering processes. To demonstrate the practical performance of the filter, we processed a set of prestack ultra-shallow seismic data recorded from a shallow fault zone and a stacked section of ground-penetrating radar data as real examples. Examining the performances of the two filters both in time and frequency domains, we notice that the recovery of the original signal depends on the attribute, intensity, and density of the noise. Inspecting the filtered synthetic records in t-x domain, these two filters not only successfully remove the random noise but also suppress the ground roll. With the prestack seismic field data, the median filter renders better resolution than the Wiener filter, but it also suppresses signals that may have geological implications, making the result less desirable. In addition, both the adaptive filters improve the geologically interesting low frequency components of the stacked ground-penetrating radar data, but the high frequency components are blurred.

AB - Adaptive filtering is an effective method to suppress speckle noise in 2D digital image data. Recently, a variety of adaptive filtering algorithms have been developed and employed to remove random noise from geophysical data. In this paper, two filters are designed by adopting adaptive algorithms, the optimum 2D median filter, (a 2D median filter with an optimum window size), and the 2D adaptive Wiener filter (a real time optimal filter renovated from the conventional Wiener filter technology) to investigate the advantages of using adaptive filters in processing ultra-shallow seismic and ground-penetrating radar data. Synthetic common-shot record with added white Gaussian noise was employed to test the effects of 2D window size on both filtering processes. To demonstrate the practical performance of the filter, we processed a set of prestack ultra-shallow seismic data recorded from a shallow fault zone and a stacked section of ground-penetrating radar data as real examples. Examining the performances of the two filters both in time and frequency domains, we notice that the recovery of the original signal depends on the attribute, intensity, and density of the noise. Inspecting the filtered synthetic records in t-x domain, these two filters not only successfully remove the random noise but also suppress the ground roll. With the prestack seismic field data, the median filter renders better resolution than the Wiener filter, but it also suppresses signals that may have geological implications, making the result less desirable. In addition, both the adaptive filters improve the geologically interesting low frequency components of the stacked ground-penetrating radar data, but the high frequency components are blurred.

KW - 2D median filter

KW - Adaptive Wiener filter

KW - Adaptive filtering

KW - GPR

KW - Seismic

UR - http://www.scopus.com/inward/record.url?scp=67349223520&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=67349223520&partnerID=8YFLogxK

U2 - 10.1016/j.jappgeo.2008.08.013

DO - 10.1016/j.jappgeo.2008.08.013

M3 - Article

AN - SCOPUS:67349223520

VL - 68

SP - 36

EP - 46

JO - Journal of Applied Geophysics

JF - Journal of Applied Geophysics

SN - 0926-9851

IS - 1

ER -