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• W2317460780 abstract "PreviousNext No AccessSEG Technical Program Expanded Abstracts 2010Spectral waveform classifier: method and examplesAuthors: Xin‐Gong LiPhillip L. JongZoltan SylvesterHarry GermsFa DwanAlan JacksonJack CookStephane GesbertChi‐Chin FengDonald P GriffithXin‐Gong LiShell Intern'l E&PSearch for more papers by this author, Phillip L. JongShell Intern'l E&PSearch for more papers by this author, Zoltan SylvesterShell Intern'l E&PSearch for more papers by this author, Harry GermsShell Intern'l E&PSearch for more papers by this author, Fa DwanShell Intern'l E&PSearch for more papers by this author, Alan JacksonShell Intern'l E&PSearch for more papers by this author, Jack CookShell Intern'l E&PSearch for more papers by this author, Stephane GesbertShell Intern'l E&PSearch for more papers by this author, Chi‐Chin FengShell E&PSearch for more papers by this author, and Donald P GriffithShell E&PSearch for more papers by this authorhttps://doi.org/10.1190/1.3513103 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract Commonly used averaged‐amplitudes over a short window can quickly provide an attribute for seismic interpretation, but this approach ignores the waveform of the data that holds information about stratigraphy. By taking the waveform shapes into consideration and using the self organized map (SOM; Kohonen, 2001), a waveform classifier (WFC) can extract additional geological information (Coleou et al, 2003). However, both methods require a careful selection of window sizes, which creates additional work and oftentimes makes the interpretation subjective to some degree. Spectral waveform classifier (SWFC) uses SOM to classify the spectral data obtained from a continuous wavelet transform (CWT). This automatically adjusts window sizes, which reduces user effort. Because of the multi‐scale nature of the spectra (with the influence of large‐scale information) SWFC better tolerates horizon picking errors than its non‐spectral counterparts, therefore yielding more consistent and robust classifications. Additionally, SWFC offers good delineation for some subtle features due to the sensitivity of spectra‐to‐thickness changes. Applying this methodology simultaneously to two multi‐offset data sets also demonstrates the usefulness for fluid predictions. SWFC can further be applied to a targeted zone for 3D interpretation. A few field data examples were selected to demonstrate the usefulness of SWFC in delineating subtle stratigraphic features, and predicting fluids at a horizon or in a 3D region around a target.Permalink: https://doi.org/10.1190/1.3513103FiguresReferencesRelatedDetails SEG Technical Program Expanded Abstracts 2010ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2010 Pages: 4453 publication data© 2010 Copyright © 2010 Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished: 21 Oct 2010 CITATION INFORMATION Xin‐Gong Li, Phillip L. Jong, Zoltan Sylvester, Harry Germs, Fa Dwan, Alan Jackson, Jack Cook, Stephane Gesbert, Chi‐Chin Feng, and Donald P Griffith, (2010), Spectral waveform classifier: method and examples, SEG Technical Program Expanded Abstracts : 1398-1402. https://doi.org/10.1190/1.3513103 Plain-Language Summary PDF DownloadLoading ..." @default.
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