Seismic data are usually sampled on a sparse and irregular grid due to the geological, logistical, and economic constraints
in seismic data acquisition. I have worked on the reconstruction of multi-dimensional seismic data by solving for a low-rank subspace from the observed
incomplete and corrupted data. The figure below shows a small patch of seismic data before and after reconstruction.
Particularly, I am interested in developing efficient matrix/tensor completion algorithms via randomized methods.
Below are two examples of my work:
Projected gradient methods for simultaneous source separation/imaging
Conventional seismic sources are fired in a non-overlapping fashion.
Simultaneous source acquisition entails firing more than one seismic source with small random time delays.
As a result the acquisition efficiency has been significantly improved. The challenge is to separate the signal
from each impulsive source.
I have worked on the separation and the direct imaging of simultaneous source data via rank-constrained inversion