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pro vyhledávání: '"Sen, Mrinal K"'
Autor:
Voytan, Dimitri P., Ravula, Sriram, Ardel, Alexandru, Liebman, Elad, Dhara, Arnab, Sen, Mrinal K., Dimakis, Alexandros
Seismic images often contain both coherent and random artifacts which complicate their interpretation. To mitigate these artifacts, we introduce a novel unsupervised deep-learning method based on Deep Image Prior (DIP) which uses convolutional neural
Externí odkaz:
http://arxiv.org/abs/2405.17597
Autor:
Biswas, Reetam, Sen, Mrinal K.
Full-Waveform Inversion (FWI) has now become a widely accepted tool to obtain high-resolution velocity models from seismic data. Typically, the velocity model in its discrete form is represented on a rectangular grid, and we solve for the elastic pro
Externí odkaz:
http://arxiv.org/abs/2201.09334
Radial basis function generated finite-difference (RBF-FD) methods have recently gained popularity due to their flexibility with irregular node distributions. However, the convergence theories in the literature, when applied to nonuniform node distri
Externí odkaz:
http://arxiv.org/abs/2004.06319
Autor:
Vamaraju, Janaki, Sen, Mrinal K.
Biot's theory provides a framework for computing seismic wavefields in fluid saturated porous media. Here we implement a velocity-stress staggered grid 2D finite difference algorithm to model the wave-propagation in poroelastic media. The Biot's equa
Externí odkaz:
http://arxiv.org/abs/1907.10833
Recent developments have made it possible to overcome grid-based limitations of finite difference (FD) methods by adopting the kernel-based meshless framework using radial basis functions (RBFs). Such an approach provides a meshless implementation an
Externí odkaz:
http://arxiv.org/abs/1812.06665
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While pseudospectral (PS) methods can feature very high accuracy, they tend to be severely limited in terms of geometric flexibility. Application of global radial basis functions overcomes this, however at the expense of problematic conditioning (1)
Externí odkaz:
http://arxiv.org/abs/1606.03258
Scattered data interpolation schemes using kriging and radial basis functions (RBFs) have the advantage of being meshless and dimensional independent, however, for the data sets having insufficient observations, RBFs have the advantage over geostatis
Externí odkaz:
http://arxiv.org/abs/1512.07584