Absolute acoustic-impedance estimation with L1 norm constraint and combined first and second order TV regularizations
- Song Guo (Tongji University) | Huazhong Wang (Tongji University)
- Document ID
- Society of Exploration Geophysicists
- 2018 SEG International Exposition and Annual Meeting, 14-19 October, Anaheim, California, USA
- Publication Date
- Document Type
- Conference Paper
- 2018. Society of Exploration Geophysicists
- Low frequency, Sparse, Poststack, Seismic impedance, Broadband
- 2 in the last 30 days
- 10 since 2007
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Acoustic impedance (AI) is the key elastic parameter for seismic inversion and is generally divided into background AI and relative AI for linear inversion. Ideally, to obtain the accurate absolute AI model, the estimated background AI and relative AI should be merged seamlessly. However, in practice, the intermediate frequency components of the AI model are usually poorly reconstructed, so the merged AI will suffer the error caused by the frequency gap. To remedy this error, priori information should be incorporated to narrow down the gap. With the knowledge that the reflectivity series underground is sparse, we solve an L1 norm constrained problem to seek for a more broadband reflectivity section. The absolute AI mode is then estimated with the broadband reflectivity section and the given background AI. The combined first and second order total variation (TV) regularization is introduced to preserve the geological structure of the AI model while eliminating the staircase effect caused by the single first-order TV norm. The numerical examples tested on Marmousi AI model demonstrate the effectiveness of the proposed methods.
Presentation Date: Wednesday, October 17, 2018
Start Time: 8:30:00 AM
Location: 206A (Anaheim Convention Center)
Presentation Type: Oral
|File Size||2 MB||Number of Pages||5|
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