  We consider the reflection seismology problem of recovering the locations of
interfaces and the amplitudes of reflection coefficients from seismic data,
which are vital for estimating the subsurface structure. The reflectivity
inversion problem is typically solved using greedy algorithms and iterative
techniques. Sparse Bayesian learning framework, and more recently, deep
learning techniques have shown the potential of data-driven approaches to solve
the problem. In this paper, we propose a weighted minimax-concave
penalty-regularized reflectivity inversion formulation and solve it through a
model-based neural network. The network is referred to as deep-unfolded
reflectivity inversion network (DuRIN). We demonstrate the efficacy of the
proposed approach over the benchmark techniques by testing on synthetic 1-D
seismic traces and 2-D wedge models and validation with the simulated 2-D
Marmousi2 model and real data from the Penobscot 3D survey off the coast of
Nova Scotia, Canada.
