Low-Dimensional Multi-Trace Impedance Inversion in Sparse Space with Elastic Half Norm Constraint

Sparse Trace Norm Regularization

We study the problem of estimating multiple predictive functions from a dictionary of basis functions in the nonparametric regression setting. Our estimation scheme assumes that each predictive function can be estimated in the form of a linear combination of the basis functions. By assuming that the coefficient matrix admits a sparse low-rank structure, we formulate the function estimation prob...

LP Sparse Spike Impedance Inversion

Among many approaches been made to improve interpretation of post stack seismic section, a great effort has been made aimed at increasing the resolution power of interpreting section and mapping the seismic reflection data to lithology. The purpose of sparse spike impedance inversion is aimed at this goal, to obtain a high-resolution impedance profile from low resolution or band limited seismic...

Low-rank optimization with trace norm penalty

The paper addresses the problem of low-rank trace norm minimization. We propose an algorithm that alternates between fixed-rank optimization and rank-one updates. The fixed-rank optimization is characterized by an efficient factorization that makes the trace norm differentiable in the search space and the computation of duality gap numerically tractable. The search space is nonlinear but is equ...

3D gravity data-space inversion with sparseness and bound constraints

One of the most remarkable basis of the gravity data inversion is the recognition of sharp boundaries between an ore body and its host rocks during the interpretation step. Therefore, in this work, it is attempted to develop an inversion approach to determine a 3D density distribution that produces a given gravity anomaly. The subsurface model consists of a 3D rectangular prisms of known sizes ...

Reasoning about Trace Dependencies in a Multi-Dimensional Space