Focusing AVO inversion based on the minimum gradient support regularization

Generally, regularization methods are adopted to decrease the non-uniqueness and instability of AVO inversion. In order to solve fuzzy boundaries and low focusing, the minimum gradient support (MGS), as one of regularization methods, is introduced to carry out pre-stack three-term AVO inversion for the first time in the paper. Then, considering the different orders of magnitude of P-impedance, S-impedance and density, we extend the traditional univariate MGS into trivariate MGS. In addition, in order to render the inversion more stable, a low-frequency constraint is also introduced to the objective function. Then, 1-D and 2-D models are designed to test the adaptability and reliability of the method. Numerical model applications show that the inversion method based on MGS is superior to the traditional model-based AVO inversion in preserving sharp boundaries. Furthermore, inverted results from MGS AVO inversion have a higher resolution than those from the traditional method. In the meantime, although the synthetic data is contaminated by noise, reasonable and reliable results can still be obtained from MGS inversion. All advantages guarantee that the focusing MGS AVO inversion will have a great potential for real data application in the near future. Presentation Date: Thursday, September 28, 2017 Start Time: 8:30 AM Location: 370D Presentation Type: ORAL