Spike-Like Blending Noise Attenuation Using Structural Low-Rank Decomposition
| Autor: | Guoning Wu, Jianyong Xie, Hanming Chen, Chaojun Shi, Yatong Zhou, Yangkang Chen |
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| Rok vydání: | 2017 |
| Předmět: |
Computer science
Speech recognition Attenuation 0211 other engineering and technologies Noise attenuation 02 engineering and technology 010502 geochemistry & geophysics Geotechnical Engineering and Engineering Geology 01 natural sciences Physics::Geophysics Matrix decomposition Robustness (computer science) Electrical and Electronic Engineering Algorithm 021101 geological & geomatics engineering 0105 earth and related environmental sciences |
| Zdroj: | IEEE Geoscience and Remote Sensing Letters. 14:1633-1637 |
| ISSN: | 1558-0571 1545-598X |
| Popis: | Spikelike noise is a common type of random noise existing in many geoscience and remote sensing data sets. The attenuation of spike-like noise has become extremely important recently, because it is the main bottleneck when processing the simultaneous source data that are generated from the modern seismic acquisition. In this letter, we propose a novel low-rank decomposition algorithm that is effective in rejecting the spike-like noise in the seismic data set. The specialty of the low-rank decomposition algorithm is that it is applied along the morphological direction of the seismic data sets with a prior knowledge of the morphology of the seismic data, which we call local slope. The seismic data are of much lower rank along the morphological direction than along the space direction. The morphology of the seismic data (local slope) is obtained via a robust plane-wave destruction method. We use two simulated field data examples to illustrate the algorithm workflow and its effective performance. |
| Databáze: | OpenAIRE |
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