Dreamlet-based interpolation using POCS method

Wang, Benfeng; Wu, Ru‐Shan; Geng, Yushui; Chen, Xiaohong

doi:10.1016/j.jappgeo.2014.08.008

Cited by 51 publications

(8 citation statements)

References 32 publications

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“…Our procedure for matching CT‐inverted density to well log bulk density first generates average values of electron density index slice by slice, interpolating missing values and smoothing this new data to match the well log resolution. The method of projection onto convex sets (POCS) (Abma & Kabir, ; Wang et al, ) was used to reconstruct missing data inside and between cores. We chose a rectangular moving average filter for the smoothing process and found a good filter kernel to have a length equal to 60 cm.…”

Section: Resultsmentioning

confidence: 99%

Monte Carlo Approach for Estimating Density and Atomic Number From Dual‐Energy Computed Tomography Images of Carbonate Rocks

2017

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We develop a new Monte Carlo‐based inversion method for estimating electron density and effective atomic number from 3‐D dual‐energy computed tomography (CT) core scans. The method accounts for uncertainties in X‐ray attenuation coefficients resulting from the polychromatic nature of X‐ray beam sources of medical and industrial scanners, in addition to delivering uncertainty estimates of inversion products. Estimation of electron density and effective atomic number from CT core scans enables direct deterministic or statistical correlations with salient rock properties for improved petrophysical evaluation; this condition is specifically important in media such as vuggy carbonates where CT resolution better captures core heterogeneity that dominates fluid flow properties. Verification tests of the inversion method performed on a set of highly heterogeneous carbonate cores yield very good agreement with in situ borehole measurements of density and photoelectric factor.

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Section: Resultsmentioning

confidence: 99%

Monte Carlo Approach for Estimating Density and Atomic Number From Dual‐Energy Computed Tomography Images of Carbonate Rocks

2017

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“…The existing POCS algorithms for seismic interpolation, which rely on sparse representation as shown in equation 4, are actually special cases of the POCS framework. For example, the POCS algorithm-based Fourier transform (Abma and Kabir, 2006), curvelet transform (Yang et al, 2012), dreamlet transform (Wang et al, 2014), and seislet transform (Gan et al, 2015) all perform the noise attenuation by thresholding the representation coefficients in a sparse transform domain. The dictionary-based seismic interpolation methods Liu et al, 2017) also fall into this scope by thresholding the dictionary sparse representation coefficients.…”

Section: Background and The Pocs Frameworkmentioning

confidence: 99%

Can learning from natural image denoising be used for seismic data interpolation?

Zhang

Yang

2020

GEOPHYSICS

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We propose an interpolation method based on the denoising convolutional neural network (CNN) for seismic data. It provides a simple and efficient way to break though the problem of scarcity of geophysical training labels which are often required by deep learning methods.This new method consists of two steps: (1) training a set of CNN denoisers to learn denoising from natural image noisy-clean pairs; and (2) integrating the trained CNN denoisers into the project onto convex set (POCS) framework to perform seismic data interpolation. We call it the CNN-POCS method. This method alleviates the demands of seismic big data which requires shared similar features in the applications of end-to-end deep learning on seismic data interpolation. Additionally, the proposed method is flexible for different types of missing traces because the missing or down-sampling locations are not involved in the training step; thus, it is of a plug-and-play nature. These indicate the high generalizability of the proposed method and a reduction in the necessity of problem-specific training. The 1 arXiv:1902.10379v2 [physics.geo-ph] 17 Apr 2019 primary results on synthetic and field data show promising interpolation performances of the proposed CNN-POCS method in terms of signal-to-noise ratio, dealiasing, and weakfeature reconstruction, in comparison with the traditional f -x prediction filtering method, curvelet transform method, and block-matching and 3D filtering (BM3D) method.2

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“…Seismic interpolation methods can be divided into four categories (Gao et al 2012;Wang et al 2014b): signal analysis and mathematical transform based methods (Naghizadeh & Sacchi 2010b;Xu et al 2010;Naghizadeh & Innanen 2011;Gao et al 2012;Wu et al 2013;Xue et al 2014); prediction filters based methods (Spitz 1991;Naghizadeh & Sacchi 2007); wave equation based methods (Ronen 1987) and rank reduction based methods (Gao et al 2013;Kreimer et al 2013;Ma 2013). Missing traces and random noise can increase the rank of matrix which is composed of seismic data at a given frequency, and the interpolated data can be obtained through rank reduction.…”

Section: Introductionmentioning

confidence: 99%

“…But it is time consuming because a bank of compactly supported filters should be obtained first from observed seismic data through self-learning, which limits its wider applications. Dreamlet transform (Geng et al 2009;Wu et al 2013;Wang et al 2014b) uses a physical wavelet as the basic atom that satisfies wave equation automatically, and can represent seismic data sparsely and efficiently compared with curvelet transform (Wang et al 2014b). Therefore, dreamlet transform is adopted as a sparse transform to decompose seismic data in this paper.…”

Section: Introductionmentioning

confidence: 99%

“…Curvelet transform (Candes et al 2006), which is a sparse transform and works better for curved seismic events compared with Fourier-based methods, is also used (Yang et al 2012;. Dreamlet transform is also used for seismic data interpolation with the POCS method (Wang et al 2014b) and the performance is better than curvelet-based method. While the POCS method has difficulty in noisy data interpolation, because it inserts the noisy observed data in every iteration, and in order to weaken the noise effects, the weighted strategy is used to reconstruct seismic data (Gao et al 2012).…”

Section: Introductionmentioning

confidence: 99%

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Simultaneous seismic data interpolation and denoising with a new adaptive method based on dreamlet transform

Wang

Chen³

et al. 2015

Geophysical Journal International

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135

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Interpolation and random noise removal is a prerequisite for multichannel techniques because the irregularity and random noise in observed data can affect their performances. Projection Onto Convex Sets (POCS) method can better handle seismic data interpolation if the data's signal-to-noise ratio (SNR) is high, while it has difficulty in noisy situations because it inserts the noisy observed seismic data in each iteration. Weighted POCS method can weaken the noise effects, while the performance is affected by the choice of weight factors and is still unsatisfactory. Thus, a new weighted POCS method is derived through the Iterative Hard Threshold (IHT) view, and in order to eliminate random noise, a new adaptive method is proposed to achieve simultaneous seismic data interpolation and denoising based on dreamlet transform. Performances of the POCS method, the weighted POCS method and the proposed method are compared in simultaneous seismic data interpolation and denoising which demonstrate the validity of the proposed method. The recovered SNRs confirm that the proposed adaptive method is the most effective among the three methods. Numerical examples on synthetic and real data demonstrate the validity of the proposed adaptive method.

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Dreamlet-based interpolation using POCS method

Cited by 51 publications

References 32 publications

Monte Carlo Approach for Estimating Density and Atomic Number From Dual‐Energy Computed Tomography Images of Carbonate Rocks

Monte Carlo Approach for Estimating Density and Atomic Number From Dual‐Energy Computed Tomography Images of Carbonate Rocks

Can learning from natural image denoising be used for seismic data interpolation?

Simultaneous seismic data interpolation and denoising with a new adaptive method based on dreamlet transform

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