Name
Abstract | ||
|---|---|---|
In this article we present a new method for seismic image restoration. When observed, a seismic image is the result of an initial deposit system that has been transformed by a set of successive geological deformations (flexures, fault slip, etc) that occurred over a large period of time. The goal of seismic restoration consists in inverting the deformations to provide a resulting image that depicts the geological deposit system as it was in a previous state. Providing a tool that quickly generates restored images helps the geophysicists to recognize geological features that may be too strongly altered in the observed image. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.procs.2013.05.237 | Procedia Computer Science |
Keywords | Field | DocType |
Seismic image restoration,Least squares minimization process,Gauss-Newton solver | Computer vision,Mathematical optimization,Computer science,Slip (materials science),Minification,Shape optimization,Artificial intelligence,Image restoration,Non-linear least squares | Conference |
Volume | ISSN | Citations |
18 | 1877-0509 | 0 |
PageRank | References | Authors |
0.34 | 4 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mathieu Gilardet | 1 | 0 | 0.34 |
| Sébastien Guillon | 2 | 33 | 6.27 |
| Bruno Jobard | 3 | 396 | 29.58 |
| Dimitri Komatitsch | 4 | 339 | 22.87 |