Non-parametric seismic data recovery with curvelet frames

By Felix J. Herrmann, Gilles Hennenfent. 2008

532 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Geophysics

View Paper: https://www.semanticscholar.org/paper/e4c58ec2f95c9d641a3cd821c2fd9e391e69ad26

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In Computational Mechanics

In Computer Vision and Pattern Recognition

In Geophysics

Citations in the corpus

Seismic data restoration via data-driven tight frame (2014, background): “…2003; Liu and Sacchi, 2004; Xu et al., 2005; Abma and Kabir, 2006; Trad, 2009), seislet transform (Fomel and Liu, 2010; Liu and Fomel, 2010), and curvelet transform (Hennenfent and Herrmann, 2008; Herrmann and Hennenfent, 2008; Naghizadeh and Sacchi, 2010; Wang et al., 2011; Shahidi et al., 2013).”
Multidimensional simultaneous random plus erratic noise attenuation and interpolation for seismic data by joint low-rank and sparse inversion (2015, methodology): “…the real spatial coordinates (Xu et al., 2005, 2010; Poole, 2010), projection onto convex sets (POCS) (Abma and Kabir, 2006), and techniques based on local transforms such as the Radon transform (Trad et al., 2002) or the curvelet transform (Herrmann and Hennenfent, 2008; Hennenfent et al., 2010).”
Intelligent Bearing Fault Diagnosis Method Combining Compressed Data Acquisition and Deep Learning (2018, background): “camera [23], magnetic resonance imaging (MRI) [24], seismic images [25], [26], radar imaging [27], and electrocardio-”
Seismic data reconstruction via matrix completion (2013, methodology): “The idea of L1-norm minimization was applied in ([5], [6], [8]) to reconstruct the missing traces of seismic data.”
A tensor higher-order singular value decomposition for prestack seismic data noise reduction and interpolation (2012, methodology): “We will adopt the notation used in Kolda and Bader (2009). The order or dimensionality of a tensor is denoted N.”
Efficient matrix completion for seismic data reconstruction (2015, methodology): “For details on the curvelet-based reconstruction techniques, we refer to (Herrmann and Hennenfent, 2008; Mansour et al., 2013).”
Nonequispaced curvelet transform for seismic data reconstruction: A sparsity-promoting approach (2010, methodology): “For more details on jittered sampling of seismic data, we refer to Hennenfent and Herrmann (2008) and Tang et al. (2009).”
Recovery of seismic wavefields based on compressive sensing by an l1-norm constrained trust region method and the piecewise random subsampling (2011, methodology): “Another sampling technique is the jittered undersampling (Hennenfent & Herrmann 2008).”
Compressive sensing: From theory to applications, a survey (2013, background): “Seismic images are neither sparse nor compressible in strict sense but are compressible in transform domains e.g., in curvelet basis [65].”
Dreamlet-based interpolation using POCS method (2014, methodology): “Methods for interpolation can be divided into three categories (Gao et al., 2013): First, methods based onmathematical transform and signal analysis (Herrmann and Hennenfent, 2008; Naghizadeh and Innanen, 2011; Naghizadeh and Sacchi, 2010; Trad et al., 2002; Wang et al., 2010); second, prediction…”
Edge-preserving noise reduction based on Bayesian inversion with directional difference constraints (2013, background): “…distribution have been widely presented for acoustic impedance inversion (e.g. Oldenburg et al 1983), sparse deconvolution (e.g. Sacchi 1997), seismic data interpolation (e.g. Herrmann and Hennenfent 2008), seismic imaging (e.g. Herrmann and Li 2012) and full-waveform inversion (e.g. Guitton 2012).”
The Curvelet Transform (2010, methodology): “Currently, a few researchers have applied the ICT method to compressed sensing in seismic data recovery [33], [34], [65], and remote sensing [44], [46].”