Sorry, you need to enable JavaScript to visit this website.
Menu
Login
  • Home
  • Irregular spatial sampling and rank reduction: interpolation by joint low-rank and sparse inversion Irregular spatial sa ...
Technical Abstract

Irregular spatial sampling and rank reduction: interpolation by joint low-rank and sparse inversion

Back to Technical Content
Until now, noise attenuation and interpolation processes based on rank reduction needed spatially regular, or at least binned, data. Here, we show how the low-rank signal model in joint low-rank and sparse inversion (JLRSI), a recently proposed convex optimization framework for simultaneous random plus erratic noise attenuation and interpolation, can be extended to spatially irregular data by appropriately modifying the inverse problem formulation. Benefits of considering the true spatial locations of seismic traces for the quality of the signal reconstruction are illustrated on a three-dimensional regularization and interpolation example on real land data.
Download Resource

Publications

EAGE - European Association of Geoscientists and Engineers

Authors

Raphael Sternfels, Anthony Prescott, Geoffroy Pignot, Longzhang Tian, David Le Meur

Month

May

Copyright

© 2016 EAGE