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

• URL: http://arxiv.org/abs/2105.00003v1
• Date: Sat, 1 May 2021 04:33:02 GMT
• Title: NuSPAN: A Proximal Average Network for Nonuniform Sparse Model -- Application to Seismic Reflectivity Inversion
• Authors: Swapnil Mache, Praveen Kumar Pokala, Kusala Rajendran and Chandra Sekhar Seelamantula
• Abstract summary: We solve the problem of proximal deconvolution in the context of high-resolution recovery of seismic data. We employ a combination of convex and non-uniform signalizers. The resulting sparse network architecture can be acquired in a data-driven fashion.
• Score: 23.080395291046408
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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