f-x adaptive seismic-trace interpolation

By Mostafa Naghizadeh, Mauricio D. Sacchi. 2009

131 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Geophysics

View Paper: https://www.semanticscholar.org/paper/7d1a9c26a29d2422665a67fd6b50a901951b2fc0

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. Xiaohong Chen
2. Mauricio D. Sacchi
3. Guochang Liu
4. Yang Liu
5. Mostafa Naghizadeh
6. Jianjun Gao
7. Sergey Fomel
8. Jing Du

In Geophysics

1. Xiaohong Chen
2. Guochang Liu
3. Mauricio D. Sacchi
4. Sergey Fomel
5. Yang Liu
6. Jianjun Gao
7. Mostafa Naghizadeh
8. Jing Du

Citations in the corpus

• A fast reduced-rank interpolation method for prestack seismic volumes that depend on four spatial dimensions (2013, methodology): “A second category groups methods based on prediction filters (Spitz, 1991; Porsani, 1999; Gulunay, 2003; Naghizadeh and Sacchi, 2007, 2009) which use the predictability of linear events in the frequencyspace domain to interpolate aliased data at high frequencies with filters derived from nonaliased…”
• Noncausal f–x–y regularized nonstationary prediction filtering for random noise attenuation on 3D seismic data (2013, methodology): “Naghizadeh and Sacchi (2009) proposed an adaptive f -x prediction filter, which was used to interpolate waveforms that have spatially variant dips.”
• Seismic data interpolation and denoising in the frequency-wavenumber domain (2012, methodology): “Another class of f-x domain seismic-trace interpolation methods have been proposed by Spitz (1991), Porsani (1999), and Naghizadeh and Sacchi (2007, 2009).”
• Multicomponent f-x seismic random noise attenuation via vector autoregressive operators (2012, methodology): “Application of the VAR de-noising method to seismic sections with curved events requires spatial windowing in order to satisfy the linear seismic events assumption (Naghizadeh and Sacchi, 2009).”
• Seismic data interpolation beyond aliasing using regularized nonstationary autoregression (2011, methodology): “Naghizadeh and Sacchi (2009) propose an adaptive f -x interpolation using exponentially weighted recursive least squares.”
• Random noise attenuation using f-x regularized nonstationary autoregression (2012, methodology): “Naghizadeh and Sacchi (2009) used exponentially weighted recursive least square (EWRLS) to solve the adaptive problem and applied it to seismic trace interpolation.”