Hybrid Seismic Denoising Using Higher-Order Statistics and Improved Wavelet Block Thresholding

By S. M. Mousavi, Charles A. Langston. 2016

186 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Geophysics

View Paper: https://www.semanticscholar.org/paper/9d851506b3ab23fb1b40912353713837f2165ddf

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Siwei Yu
2. Jianwei Ma
3. Gregory C. Beroza
4. Weiqiang Zhu
5. Y. Sheng
6. S. M. Mousavi
7. Xiaoqun Zhang

In Geophysics

1. Siwei Yu
2. S. M. Mousavi
3. Gregory C. Beroza
4. Jianwei Ma
5. Weiqiang Zhu
6. Y. Sheng
7. P. Martin Mai
8. Xiaoqun Zhang

Citations in the corpus

• Deep Learning for Geophysics: Current and Future Trends (2021, background): “The sparsity assumption presumes that the data are sparse under certain transforms (Donoho & Johnstone, 1995), such as the curvelet domain (Herrmann & Henn-enfent, 2008) or other time-frequency domains (Mousavi, Langston, et al., 2016; Mousavi & Langston 2016, 2017).”
• STanford EArthquake Dataset (STEAD): A Global Data Set of Seismic Signals for AI (2019, methodology): “The de-signaling algorithm used here is a combination of the methods introduced in [24] and [25] that identifies the anomalous spectral features associated with earthquake signals (based on statistical considerations) in a continuous wavelet domain.”