Comparative study of new signal processing to improve S/N ratio of seismic data

A prime target of seismic data processing is to improve the signal-to-noise ratio of the seismic data. New signal processing tools such as Wavelet transform, Radon transform, Fan-beam transform, Ridgelet transform and Curvelet transform have proven their results in image processing. A comparative s...

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Main Authors: Sajid, Muhammad, Ghosh, Deva P., Satti, Ifthikar
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.11540 /
Published: 2013
Subjects:
QE Geology
Online Access: http://eprints.utp.edu.my/11540/1/Comparative%20study%20of%20new%20signal%20processing%20to%20improve%20S%20N%20ratio%20of%20seismic%20data.pdf
http://eprints.utp.edu.my/11540/
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spelling utp-eprints.115402017-03-20T01:59:26Z Comparative study of new signal processing to improve S/N ratio of seismic data Sajid, Muhammad Ghosh, Deva P. Satti, Ifthikar QE Geology A prime target of seismic data processing is to improve the signal-to-noise ratio of the seismic data. New signal processing tools such as Wavelet transform, Radon transform, Fan-beam transform, Ridgelet transform and Curvelet transform have proven their results in image processing. A comparative study has been performed with these techniques to test their ability to increase the signal-to-noise ratio of seismic data by removing random noises. We then described the comprehensive mathematical formulation of these algorithms and tested them on both synthetic seismic data, which was created with a known signal-to-noise ratio with desired geologic features, and real seismic data, which contained curved features with random noise. Wavelet transform, which extends the robustness of frequency dependent filtering by adding time dimension and multiscale wavelet translation, improves the signal-to noise-ratio through the threshold coefficient filtering of random noise. The Radon transform and Fan-beam transform provide the opportunity of angle-dependent filtering, but produce adverse effects on curved features of seismic data and decrease seismic resolution. Ridgelet and Curvelet transform are more robust than Radon and Fan-beam transform. But Ridgelet transform, which uses Radon transform in its coefficient calculation, also produces adverse effects on curved features and threshold filtering leads to a decrease in the signal-to-noise ratio. The results have shown that theCurvelet transform is robust enough to handle random noise and also preserve the inclined and curved features of seismic data. However, its coefficient calculation requires large computation time and memory space 2013 Article PeerReviewed application/pdf http://eprints.utp.edu.my/11540/1/Comparative%20study%20of%20new%20signal%20processing%20to%20improve%20S%20N%20ratio%20of%20seismic%20data.pdf Sajid, Muhammad and Ghosh, Deva P. and Satti, Ifthikar (2013) Comparative study of new signal processing to improve S/N ratio of seismic data. ORIGINAL PAPER - EXPLORATION GEOPHYSICS . http://eprints.utp.edu.my/11540/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
topic QE Geology
spellingShingle QE Geology
Sajid, Muhammad
Ghosh, Deva P.
Satti, Ifthikar
Comparative study of new signal processing to improve S/N ratio of seismic data
description A prime target of seismic data processing is to improve the signal-to-noise ratio of the seismic data. New signal processing tools such as Wavelet transform, Radon transform, Fan-beam transform, Ridgelet transform and Curvelet transform have proven their results in image processing. A comparative study has been performed with these techniques to test their ability to increase the signal-to-noise ratio of seismic data by removing random noises. We then described the comprehensive mathematical formulation of these algorithms and tested them on both synthetic seismic data, which was created with a known signal-to-noise ratio with desired geologic features, and real seismic data, which contained curved features with random noise. Wavelet transform, which extends the robustness of frequency dependent filtering by adding time dimension and multiscale wavelet translation, improves the signal-to noise-ratio through the threshold coefficient filtering of random noise. The Radon transform and Fan-beam transform provide the opportunity of angle-dependent filtering, but produce adverse effects on curved features of seismic data and decrease seismic resolution. Ridgelet and Curvelet transform are more robust than Radon and Fan-beam transform. But Ridgelet transform, which uses Radon transform in its coefficient calculation, also produces adverse effects on curved features and threshold filtering leads to a decrease in the signal-to-noise ratio. The results have shown that theCurvelet transform is robust enough to handle random noise and also preserve the inclined and curved features of seismic data. However, its coefficient calculation requires large computation time and memory space
format Article
author Sajid, Muhammad
Ghosh, Deva P.
Satti, Ifthikar
author_sort Sajid, Muhammad
title Comparative study of new signal processing to improve S/N ratio of seismic data
title_short Comparative study of new signal processing to improve S/N ratio of seismic data
title_full Comparative study of new signal processing to improve S/N ratio of seismic data
title_fullStr Comparative study of new signal processing to improve S/N ratio of seismic data
title_full_unstemmed Comparative study of new signal processing to improve S/N ratio of seismic data
title_sort comparative study of new signal processing to improve s/n ratio of seismic data
publishDate 2013
url http://eprints.utp.edu.my/11540/1/Comparative%20study%20of%20new%20signal%20processing%20to%20improve%20S%20N%20ratio%20of%20seismic%20data.pdf
http://eprints.utp.edu.my/11540/
_version_ 1741196136994045952
score 11.62408

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