Full-waveform inversion with fractality information of the subsurface
- Pedro Carvalho (Federal University of Rio Grande do Norte (UFRN)) | Gilberto Corso (Federal University of Rio Grande do Norte (UFRN)) | Sérgio da Silva (Federal University of Rio Grande do Norte (UFRN)) | Joáo de Arájo (Federal University of Rio Grande do Norte (UFRN)) | Liacir Lucena (Federal University of Rio Grande do Norte (UFRN)) | Yanghua Wang (Imperial College London)
- Document ID
- Society of Exploration Geophysicists
- SEG International Exposition and Annual Meeting, 15-20 September, San Antonio, Texas, USA
- Publication Date
- Document Type
- Conference Paper
- 2019. Society of Exploration Geophysicists
- Imaging, Fractals, Acoustic, Full-waveform inversion, Statistics
- 1 in the last 30 days
- 1 since 2007
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Although full-waveform inversion (FWI) is a powerful seismic imaging technique, it still faces some difficulties, mainly because it is an ill-posed inverse problem, nonlinear and with many variables. Several methods and strategies have been proposed to mitigate these difficulties. However, most of these approaches are based only on information contained in the recorded seismograms and therefore, bearing in mind that the recorded data are incomplete (e.g., with a lack of information regarding low frequencies), most of these approaches are also hostage to the poor quality of the recorded data, preventing them from reaching more accurate images of the subsurface. Thus, in addition to the information from the recorded data, it is imperative to add more information in FWI. In this work, the inclusion of the knowledge regarding correlations found between the P-wave velocities characterized by fractional Brownian motions (fBm) is investigated and evaluated the influence of the assumption of different kind of fBm on the results of the FWI. We have demonstrated that the inclusion of this knowledge can contribute greatly to help FWI to achieve more accurate images of the subsurface, with potential advantages over powerful regularization techniques such as the Total Variation regularization method (TV). In view of the results, it leads us to suspect that the most accurate images of the subsurface may be achieved not for true subsurface Hurst indexes but for Hurst indexes that surround the true ones.
Presentation Date: Monday, September 16, 2019
Session Start Time: 1:50 PM
Presentation Time: 3:55 PM
Location: Poster Station 9
Presentation Type: Poster
|File Size||974 KB||Number of Pages||5|
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