Target-oriented least-squares migration/inversion with sparseness constraints |

**Yaxun Tang**

I pose the seismic imaging problem as an inverse problem and present a regularized inversion scheme that tries
to overcome three main practical issues with the standard least-squares migration/inversion (LSI) approach, i.e.,
the high computational cost, the operator mismatch, and the poorly constrained solution due to a limited surface acquisition geometry.
I show that the computational cost is considerably reduced by formulating the LSI problem in a target-oriented fashion
and computing a truncated Hessian operator using the phase-encoding method.
The second and third issues are mitigated by introducing a non-quadratic regularization operator that imposes
sparseness to the model parameters. Numerical examples on the Marmousi model
show that the sparseness constraint has the potential to effectively reduce the null space and produce
an image with high resolution, but it also has the risk of over-penalizing weak reflections.

- introduction
- Target-oriented least-squares migration
- Hessian by phase encoding
- regularization with sparseness constraints
- numerical examples
- discussion
- conclusions
- Bibliography
- About this document ...

Target-oriented least-squares migration/inversion with sparseness constraints |

2009-05-05