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We propose a conceptually simple and efficient algorithm to perform robust inversion on noisy data. The method has two distinctive aspects. One, it can effectively ignore erroneous measurements during inversion and two, it is very easy to implement. The method works by applying a diagonal weight matrix to data residuals based on their statistics. The estimation of weights is completely automatic and relies on the assumption that the noisy measurements are statistically insignificant. In this abstract we demonstrate the application of the proposed algorithm via Radon transforms and Adaptive Optics, a technique borrowed from the field of Astrophysics for the purpose of building high-resolution velocity models.
Presentation Date: Wednesday, September 27, 2017
Start Time: 10:10 AM
Presentation Type: ORAL
Reflection-based waveform inversion introduces migration and demigration process to retrieve low-wavenumber component from reflection data, but both difference- and correlation-based methods suffer from the cycle-skipping problem when the time shifts change rapidly. In this abstract, we introduce model extension and differential semblance optimization into reflection-based waveform inversion. With another degree of freedom in the model space, we can eliminate the time difference between synthetic and observed data so the cycle skipping problem is avoided. Differential semblance operator is used to detect the incoherence of the extended model. The hybrid objective function, consisting of data misfitting and differential semblance term, shows better convexity than only data misfitting term. We propose a two-stage scheme to minimize the hybrid objective function, that is, the inner loop to update extended the reflectivity and the outer loop to update the background velocity. In order to accelerate the convergent rate of the two-stage scheme, we propose two different approximations of diagonal Hessian and use them as preconditioners in the inner and outer loop respectively. With numerical tests, we show the importance of linearized inversion in the inner loop and also demonstrate that our proposed method can successfully recover both high- and low-wavenumber components of the subsurface model. Even though we specify the model extension in the subsurface offset domain and ignore non-linear effects, other extensions and non-linear inversion are also possible under the same framework.
With recent advances in high-end borehole geophysics, it might be prudent to question what is actually necessary to achieve business value and whether such practices are currently in place. Here, “high-end” borehole geophysics essentially goes beyond the standard time-to-depth measurement with major service providers, such as three-dimensional (3D) imaging, time-lapse reservoir monitoring, passive (micro-seismic) monitoring, and surface seismic (anisotropic) velocity model calibration. Instead, this study focuses primarily on marine 3D vertical seismic profiling (VSP) imaging and reservoir monitoring applications, with consideration to other applications.
Presentation Date: Monday, September 25, 2017
Start Time: 3:30 PM
Presentation Type: ORAL
We present a technique for reconstructing subsurface model changes from time-lapse seismic survey data using full-waveform inversion (FWI). The technique is based on simultaneously inverting multiple survey vintages, with regularization of the model difference. In addition to the fully simultaneous FWI that requires the solution of a larger optimization problem, we propose a simplified cross-updating workflow that can be implemented using the existing FWI tools. The proposed methods are demonstrated on synthetic examples, and their robustness with regard to repeatability issues is compared to alternative techniques, such as parallel, sequential, and double-difference methods.