Name
Abstract | ||
|---|---|---|
Random noise in seismic data can affect the performance of reservoir characterization and interpretation, which makes denoising become an essential procedure. This letter focuses on suppressing random noise in poststack seismic data while preserving the edges of desired signals. Due to the lateral continuity of seismic data, polynomial fitting (PF) method can be a good alternative in attenuating r... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/LGRS.2017.2685631 | IEEE Geoscience and Remote Sensing Letters |
Keywords | Field | DocType |
Principal component analysis,Estimation,Robustness,Noise reduction,Signal to noise ratio,Transforms,Algorithm design and analysis | Noise reduction,Polynomial,Robustness (computer science),Artificial intelligence,Computer vision,Classification of discontinuities,Algorithm design,Signal-to-noise ratio,Algorithm,Reservoir modeling,Machine learning,Principal component analysis,Mathematics | Journal |
Volume | Issue | ISSN |
14 | 6 | 1545-598X |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu-Chen Wang | 1 | 34 | 27.05 |
| Wenkai Lu | 2 | 28 | 16.27 |
| Benfeng Wang | 3 | 4 | 7.52 |