Name
Abstract | ||
|---|---|---|
For many practical applications, it is important to solve the seismic inverse problem, i.e., to measure seismic travel times and reconstruct velocities at different depths from these data. The existing algorithms for solving the seismic inverse problem often take too long and/or produce un-physical results - because they do not take into account the knowledge of geophysicist experts. In this paper, we analyze how expert knowledge can be used in solving the seismic inverse problem. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1016/j.ijar.2006.06.025 | International Journal of Approximate Reasoning |
Keywords | Field | DocType |
geophysicist expert,existing algorithm,expert knowledge,different depth,practical application,seismic travel time,seismic inverse problem,un-physical result,predictive models,approximation algorithms,inverse problems,earth,inverse problem,geology,uncertainty,knowledge engineering | Approximation algorithm,Computer science,Expert system,Knowledge engineering,Inverse problem,Artificial intelligence,Travel time,Machine learning | Journal |
Volume | Issue | ISSN |
45 | 3 | International Journal of Approximate Reasoning |
ISBN | Citations | PageRank |
0-7803-9187-X | 0 | 0.34 |
References | Authors | |
5 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| matthew george averill | 1 | 5 | 1.10 |
| katherine miller | 2 | 0 | 0.68 |
| G. Randy Keller | 3 | 0 | 0.34 |
| Vladik Kreinovich | 4 | 1091 | 281.07 |
| Roberto Araiza | 5 | 32 | 4.64 |
| Scott A. Starks | 6 | 61 | 12.76 |