Seismic trace interpolation in the f‐x‐y domain

By Yanghua Wang. 2002

129 citations recorded by the source corpus.

Seismic trace interpolation is implemented as a 2‐D (x, y) spatial prediction, performed separately on each frequency (f) slice. This so‐called f‐x‐y domain trace interpolation method is based on the relation that the linear prediction (LP) operator estimated at a given frequency may be used to predict data at a higher frequency but a smaller trace spacing. The relationship originally given for thef‐x domain trace interpolation is successfully extended to the f‐x‐y domain. The extension is achieved by masking the data samples selectively from the input frequency slice to design the LP operators. Two interpolation algorithms using the full‐step and the fractional‐step predictions, respectively, are developed. Both methods use an all‐azimuth prediction in the x‐y domain, but the fractional‐step prediction method is computationally more efficient. While the interpolation method can be applied to a common‐offset cube of 3‐D seismic, it can also be applied to 2‐D seismic traces for prestack data processing. Sy...

Subfields: Geophysics, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/2a586cfb99fe3e79d9ac9ba53987fb7d38249d03

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Citations in the corpus

Simply denoise: Wavefield reconstruction via jittered undersampling (2008, background): “The most common of these filters are the prediction error filters (PEF’s) that can handle aliased events (Spitz, 1991).”
Antileakage Fourier transform for seismic data regularization (2005, background): “The main advantage of the PEF is that it can handle aliased linear events (Spitz, 1991; Mazzucchelli et al., 1998) and nonstationary events (Crawley, 2000).”
Structure-oriented singular value decomposition for random noise attenuation of seismic data (2015, background): “…of useful signals in small spatial-temporal windows, such as f − x deconvolution (Canales, 1984; Chen and Ma, 2014), f − x − y prediction filtering (Wang, 1999, 2002), t − x prediction error filtering (Abma and Claerbout, 1995), and f − x non-stationary polynomial fitting (Liu et al., 2011).”
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).”
Curvelet-based POCS interpolation of nonuniformly sampled seismic records (2012, methodology): “Spitz (1991) proposed his classic f–x method, based on the fact that the missing traces spaced equally can be exactly interpolated by a set of linear equations.”
Seismic reflection data interpolation with differential offset and shot continuation (2003, background): “Prediction-error filters in the timespace or frequency-space domains successfully interpolate data composed of distinctive plane waves (Spitz, 1991; Claerbout, 1999).”
Seismic data interpolation beyond aliasing using regularized nonstationary autoregression (2011, background): “Huard et al. (1996) and Wang (2002) extend f -x trace interpolation to higher spatial dimensions.”
Random noise attenuation using f-x regularized nonstationary autoregression (2012, methodology): “…seismic data with additive random noise and noncausal prediction with order 2Mwhich includes both forward and backward prediction equations (Spitz, 1991; Naghizadeh and Sacchi, 2009), we can obtain εn(f) = Sn(f)− M∑ i=1 aiSn−i(f)− −M∑ i=−1 aiSn−i(f), (4) where εn(f)is a complex noise…”
Dreamlet-based interpolation using POCS method (2014, unknown): “, 2010); second, prediction filter based methods (Naghizadeh and Sacchi, 2007; Spitz, 1991); third, interpolation methods based on wave equation (Ronen, 1987).”
Tensor completion based on nuclear norm minimization for 5D seismic data reconstruction (2013, methodology): “Prediction filtering methods use the predictability of the signal in the F−X or T−X domain (Spitz, 1991; Naghizadeh and Sacchi, 2007).”