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Application of wavelet transform to seismic data; Wavelet henkan no jishin tansa eno tekiyo

Abstract

Introduced herein is the use of the wavelet transform in the field of seismic exploration. Among applications so far made, there are signal filtering, break point detection, data compression, and the solution of finite differential equations in the wavelet domain. In the field of data compression in particular, some examples of practical application have been introduced already. In seismic exploration, it is expected that the wavelet transform will separate signals and noises in data in a way different from the Fourier transform. The continuous wavelet transform displays time change in frequency easy to read, but is not suitable for the analysis and processing large quantities of data. On the other hand, the discrete wavelet transform, being an orthogonal transform, can handle large quantities of data. As compared with the conventional Fourier transform that handles only the frequency domain, the wavelet transform handles the time domain as well as the frequency domain, and therefore is more convenient in handling unsteady signals. 9 ref., 8 figs.
Authors:
Nakagami, K; Murayama, R; Matsuoka, T [1] 
  1. Japan National Oil Corp., Tokyo (Japan)
Publication Date:
May 01, 1996
Product Type:
Conference
Report Number:
CONF-9605233-
Reference Number:
SCA: 580000; 440700; PA: NEDO-96:913457; EDB-96:173374; SN: 96001687063
Resource Relation:
Conference: 94. SEGJ (The Society of Exploration Geophysicists of Japan) Conference, Butsuri tansa gakkai dai 94 kai (1996 nendo shunki) gakujutsu koenkai, Tokyo (Japan), 15-17 May 1996; Other Information: PBD: May 1996; Related Information: Is Part Of Proceedings of the 94th SEGJ (The Society of Exploration Geophysicists of Japan) Conference; PB: 475 p.; Butsuri tansa gakkai dai 94 kai (1996 nendo shunki) gakujutsu koenkai koen ronbunshu
Subject:
58 GEOSCIENCES; 44 INSTRUMENTATION, INCLUDING NUCLEAR AND PARTICLE DETECTORS; SEISMIC SURVEYS; NUMERICAL ANALYSIS; MATHEMATICAL MODELS; FOURIER TRANSFORMATION; ORTHOGONAL TRANSFORMATIONS; FILTRATION; NOISE; SIGNALS; DATA ANALYSIS; FREQUENCY RANGE
OSTI ID:
395494
Research Organizations:
Society of Exploration Geophysicists of Japan, Tokyo (Japan)
Country of Origin:
Japan
Language:
Japanese
Other Identifying Numbers:
Other: ON: DE97709027; TRN: 96:913457
Availability:
Available from The Society of Exploration Geophysicists of Japan, 2-18, Nakamagome 2-chome, Ota-ku, Tokyo, Japan; OSTI as DE97709027
Submitting Site:
NEDO
Size:
pp. 119-122
Announcement Date:

Citation Formats

Nakagami, K, Murayama, R, and Matsuoka, T. Application of wavelet transform to seismic data; Wavelet henkan no jishin tansa eno tekiyo. Japan: N. p., 1996. Web.
Nakagami, K, Murayama, R, & Matsuoka, T. Application of wavelet transform to seismic data; Wavelet henkan no jishin tansa eno tekiyo. Japan.
Nakagami, K, Murayama, R, and Matsuoka, T. 1996. "Application of wavelet transform to seismic data; Wavelet henkan no jishin tansa eno tekiyo." Japan.
@misc{etde_395494,
title = {Application of wavelet transform to seismic data; Wavelet henkan no jishin tansa eno tekiyo}
author = {Nakagami, K, Murayama, R, and Matsuoka, T}
abstractNote = {Introduced herein is the use of the wavelet transform in the field of seismic exploration. Among applications so far made, there are signal filtering, break point detection, data compression, and the solution of finite differential equations in the wavelet domain. In the field of data compression in particular, some examples of practical application have been introduced already. In seismic exploration, it is expected that the wavelet transform will separate signals and noises in data in a way different from the Fourier transform. The continuous wavelet transform displays time change in frequency easy to read, but is not suitable for the analysis and processing large quantities of data. On the other hand, the discrete wavelet transform, being an orthogonal transform, can handle large quantities of data. As compared with the conventional Fourier transform that handles only the frequency domain, the wavelet transform handles the time domain as well as the frequency domain, and therefore is more convenient in handling unsteady signals. 9 ref., 8 figs.}
place = {Japan}
year = {1996}
month = {May}
}