brad@sep.stanford.edu, antoine@sep.stanford.edu

## ABSTRACT
We explore the classic signal and noise separation problem of removing
linear events from shot-gathers through several
inversion schemes using a combined modeling operator composed of both
hyperbolic and linear radon transforms. Data are inverted
simultaneously for both linear and hyperbolic moveout which provides
two model-space outputs. These are forward modeled seperately to
provide an output data-space devoid of one of the model-space components.
We employ this approach to analyze the removal of direct arrivals and
ground-roll from shot-gathers. Inversion schemes used imnclude: 1)
bound-constrained, 2) Cauchy norm regularization, 3) Huber norm
approximating the l norm using linear
least-squares. Synthetic tests and four field shot-gathers are used
to demonstrate the approach. Methods 1, 2, and 3 are designed to provide
sparse model-space inversions. In the real data examples,
the least-squares solution is able to better achieve the signal to
noise separation goal despite its model-space being often
less appealing.
_{2} |

- Introduction
- Theory
- Synthetic examples
- Noise separation in field data
- Comments and conclusions
- REFERENCES
- About this document ...

5/3/2005