NuSPAN: A Proximal Average Network for Nonuniform Sparse Model -- Application to Seismic Reflectivity Inversion

Autor:	Mache, Swapnil, Pokala, Praveen Kumar, Rajendran, Kusala, Seelamantula, Chandra Sekhar
Rok vydání:	2021
Předmět:	Physics - Geophysics Computer Science - Machine Learning Electrical Engineering and Systems Science - Image and Video Processing Electrical Engineering and Systems Science - Signal Processing
Druh dokumentu:	Working Paper
Popis:	We solve the problem of sparse signal deconvolution in the context of seismic reflectivity inversion, which pertains to high-resolution recovery of the subsurface reflection coefficients. Our formulation employs a nonuniform, non-convex synthesis sparse model comprising a combination of convex and non-convex regularizers, which results in accurate approximations of the l0 pseudo-norm. The resulting iterative algorithm requires the proximal average strategy. When unfolded, the iterations give rise to a learnable proximal average network architecture that can be optimized in a data-driven fashion. We demonstrate the efficacy of the proposed approach through numerical experiments on synthetic 1-D seismic traces and 2-D wedge models in comparison with the benchmark techniques. We also present validations considering the simulated Marmousi2 model as well as real 3-D seismic volume data acquired from the Penobscot 3D survey off the coast of Nova Scotia, Canada. Comment: 16 pages, 13 figures. This article builds on arXiv:2104.04704. Additions to the introductory sections; references added; results unchanged
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2105.00003 Zobrazit plný text záznamu View this record from Arxiv