A new anisotropic inversion method based on the sparse representation and dictionary learning
- Yaojun Wang (University of Electronic Science and Technology of China, UESTC) | Zhenliang Zhang (University of Electronic Science and Technology of China, UESTC) | Kai Xing (University of Electronic Science and Technology of China, UESTC) | Xijun Wu (University of Electronic Science and Technology of China, UESTC) | Hua Wang (University of Electronic Science and Technology of China, UESTC) | Guangmin Hu (University of Electronic Science and Technology of China, UESTC) | Lideng Gan (CNPC)
- 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
- Anisotropy, AVO/AVA, Inversion, Machine learning
- 1 in the last 30 days
- 11 since 2007
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It is widely known that anisotropic inversion is a nonlinear ill-posed problem, which are usually simplified as a linear problem. However, the linear problem is still unstable which is usually avoided by adding a specific mathematical regularization item (suppose the parameters follow Gaussian distribution) on anisotropic inversion. The regularization item can only works for a simple model which limits the applications in subsurface complicated structure. We proposed a data-driven regularization method to improve the stability of anisotropic inversion. The method uses dictionary learning algorithm to obtain dictionary of anisotropic characteristics and elastic parameters from logs. The obtained dictionary is used to sparsely represent prior information which is eventually used to constrain the anisotropic inversion procedure. Requirement that the model conforms to a particular mathematical distribution is no longer needed in this sparse representation anisotropic inversion (SRAI) method. The only requirement is to provide sufficient logs in the same work area to learn the parameters’ characteristic. The stratigraphic characteristics in logs are contained in the inversion method which makes the inversed result approaching to the subsurface structures. The EAGE model and the field data are used to test the reliability of the algorithm.
Presentation Date: Monday, September 16, 2019
Session Start Time: 1:50 PM
Presentation Time: 3:05 PM
Presentation Type: Oral
|File Size||3 MB||Number of Pages||5|
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