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Wavelet extraction using cepstrum

Authors
Kim, Young C.Jo, YeonghwaByun, Joongmoo
Issue Date
Sep-2023
Publisher
Society of Exploration Geophysicists
Keywords
deconvolution; processing; signal processing; wavelet
Citation
Geophysics, v.88, no.5, pp V403 - V413
Indexed
SCIE
SCOPUS
Journal Title
Geophysics
Volume
88
Number
5
Start Page
V403
End Page
V413
URI
https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/203730
DOI
10.1190/geo2022-0765.1
ISSN
0016-8033
1942-2156
Abstract
Cepstrum-based deconvolution can be a powerful tool to enhance the resolution of seismic data by removing multiples or sharpening the wavelets in the seismic data. In the past, it has been tested under the assumption that subsurface reflectivity sequences are minimum phase. However, the minimum phase requirement may be too restrictive to be used for seismic data acquired for oil and gas exploration. When the reflectivity sequence is not minimum phase, phase wrapping severely impacts the phase of the wavelet with the phase of the reflectivity sequence. We have examined the effect of phase wrapping by using two wavelets and a reflectivity sequence that was not minimum phase. Upon comparing the two cepstra, the cepstrum directly from the trace and the desired cepstrum, we find that the difference between the two cepstra indicated an odd symmetry near the zero quefrency and devise a way to make the cepstrum from the trace close to the desired cepstrum if the wavelet does not undergo its own phase wrapping. This scheme allows for extracting the correct wavelet, which was used for deconvolution to recover the reflectivity sequence. We apply the scheme to a shot gather from a shallow marine environment to extract the wavelets and deconvolve the data with the extracted wavelets. The deconvolved shot gather indicates much enhanced resolution and the wavelets appear closer to zero phase than the wavelets in the gather before deconvolution. Because air-gun arrays usually generate a source wavelet that does not have its own phase wrapping, cepstrum-based approaches can be a viable tool to extract a wavelet from marine seismic data for subsequent processing.
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