A variational multi scale approach to model blood flow through arteries is proposed. A finite element discretization to represent the coarse scales (macro size), is coupled to smoothed dissipative particle dynamics that captures the fine scale features (micro scale). Blood is assumed to be incompressible, and flow is described through the Navier Stokes equation. The proposed cou- pling is tested with two benchmark problems, in fully coupled systems. Further refinements of the model can be incorporated in order to explicitly include blood constituents and non-Newtonian behavior. The suggested algorithm can be used with any particle-based method able to solve the Navier-Stokes equation.
This paper inverts space-lags in the suboffset-domain CIGs instead of time-lags for velocity estimate. As a reminder, the conventional DSO is an image-domain method for wave-equation tomography, where the essence of this method is to focus the image at zero suboffset and minimize the image at nonzero suboffset. At each iteration, the velocity model is updated by smearing the image on the nonzero sub- offset along wavepath. The space-lag is used as a penalty operator which annihilates the image energy at nonzero lags, where this space-lag is independent of the velocity model. This new method, denoted as generalized DSO, treats the space-lag as a function of velocity model. It is an extension of the conventional DSO except it updates the velocity model not only by smearing the image on the nonzero suboffset as in conventional DSO but also by smearing the nonzero suboffset along the wavepath. The former minimizes the image in the nonzero suboffset and the latter minimizes the nonzero suboffset of the image. Both methods aim to focus the image energy at zero suboffset. The mathematical derivation and numerical examples are presented to demonstrate its effectiveness in velocity inversion.
Multisource migration with frequency selection is now extended to multisource full waveform inversion (FWI) of supergathers for marine streamer data. There are three advantages of this approach compared to conventional FWI for marine streamer data. 1. The multisource FWI method with frequency selection is computationally more efficient than conventional FWI. 2. A supergather requires more than an order of magnitude less storage than the the original data. 3. Frequency selection overcomes the acquisition mismatch between the observed data and the simulated multisource supergathers for marine data. This mismatch problem has prevented the efficient application of FWI to marine geometries in the space-time domain. Preliminary result of applying multisource FWI with frequency selection to a synthetic marine data set suggests it is at least four times more efficient than standard FWI.
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