DeepAI AI Chat
Log In Sign Up

Beating level-set methods for 3D seismic data interpolation: a primal-dual alternating approach

07/09/2016
by   Rajiv Kumar, et al.
0

Acquisition cost is a crucial bottleneck for seismic workflows, and low-rank formulations for data interpolation allow practitioners to `fill in' data volumes from critically subsampled data acquired in the field. Tremendous size of seismic data volumes required for seismic processing remains a major challenge for these techniques. We propose a new approach to solve residual constrained formulations for interpolation. We represent the data volume using matrix factors, and build a block-coordinate algorithm with constrained convex subproblems that are solved with a primal-dual splitting scheme. The new approach is competitive with state of the art level-set algorithms that interchange the role of objectives with constraints. We use the new algorithm to successfully interpolate a large scale 5D seismic data volume, generated from the geologically complex synthetic 3D Compass velocity model, where 80

READ FULL TEXT

page 11

page 12

page 13

page 14

11/02/2017

Efficient Constrained Tensor Factorization by Alternating Optimization with Primal-Dual Splitting

Tensor factorization with hard and/or soft constraints has played an imp...
02/17/2017

Accelerated Primal-Dual Proximal Block Coordinate Updating Methods for Constrained Convex Optimization

Block Coordinate Update (BCU) methods enjoy low per-update computational...
06/15/2018

Primal-dual residual networks

In this work, we propose a deep neural network architecture motivated by...
06/06/2019

Primal-Dual Block Frank-Wolfe

We propose a variant of the Frank-Wolfe algorithm for solving a class of...
02/09/2016

Large scale multi-objective optimization: Theoretical and practical challenges

Multi-objective optimization (MOO) is a well-studied problem for several...
03/23/2023

Dual-Quaternion Interpolation

Transformations in the field of computer graphics and geometry are one o...