Seismic data interpolation beyond aliasing using regularized nonstationary autoregression

By Yang Liu, Sergey Fomel. 2011

73 citations recorded by the source corpus.

Subfields: Geophysics, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/eea6511fa06719f515ddb64513b9e22120371458

How researchers see this paper

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

In Geophysics

1. Xiaoqun Zhang
2. Jianwei Ma
3. Jingwei Liang
4. Xiaohong Chen
5. Guochang Liu
6. Stanley J. Osher
7. Yi Yang
8. Siwei Yu

In Mechanical Engineering

1. Stanley J. Osher
2. Xiaoqun Zhang
3. Xiaohong Chen
4. Yi Yang
5. Jianwei Ma
6. Jingwei Liang
7. Guochang Liu

Citations in the corpus

• Seismic data restoration via data-driven tight frame (2014, methodology): “The prediction-filter method can be implemented in the time-spatial t-x domain (Crawley et al., 1999; Liu and Fomel, 2011), the frequency-spatial f-x domain (Spitz, 1991; Porsani, 1999; Naghizadeh and Sacchi, 2007), the frequency-wavenumber f-x domain (Gulunay, 2003), and even the curvelet domain…”
• Noncausal f–x–y regularized nonstationary prediction filtering for random noise attenuation on 3D seismic data (2013, methodology): “The RNA method can also be used for seismic data processing in t-x-y domain, such as seismic data interpolation (Liu and Fomel, 2011). f-x-y NRNA for random noise attenuation Two dimensional f -x NRNA only considers one space coordinate x.”
• Seismic data reconstruction via matrix completion (2013, methodology): “For instance, Liu and Fomel ([12]) proposed a regularized nonstationary autoregression for adaptive error-prediction filtering; Naghizadeh and Sacchi ([13]) used multidimensional prediction filters for seismic data reconstruction and in ([7]) they proposed prediction filters using curvelet transform”