Name
Abstract | ||
|---|---|---|
Seismic data denoising is vital to geophysical applications and the transform-based function method is one of the most widely used techniques. However, it is challenging to design a suit- able sparse representation to express a transform-based func- tion group due to the complexity of seismic data. In this paper, we apply a seismic data denoising method based on learning- type overcomplete dictionaries which uses two-dimensional sparse coding (2DSC). First, we model the input seismic data and dictionaries as third-order tensors and introduce tensor- linear combinations for data approximation. Second, we ap- ply learning-type overcomplete dictionary, i.e., optimal sparse data representation is achieved through learning and training. Third, we exploit the alternating minimization algorithm to solve the optimization problem of seismic denoising. Finally we evaluate its denoising performance on synthetic seismic data and land data survey. Experiment results show that the two-dimensional sparse coding scheme reduces computational costs and enhances the signal-to-noise ratio. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Computer Vision and Pattern Recognition | Noise reduction,Linear combination,Tensor,Pattern recognition,Computer science,Neural coding,Sparse approximation,Data approximation,Artificial intelligence,Optimization problem,Sparse matrix,Machine learning |
DocType | Volume | Citations |
Journal | abs/1704.04429 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ming-Jun Su | 1 | 0 | 1.01 |
| Jingbo Chang | 2 | 0 | 0.68 |
| Feng Qian | 3 | 152 | 12.53 |
| Guang-min Hu | 4 | 87 | 19.78 |
| Xiaoyang Liu | 5 | 270 | 34.49 |