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CBD method

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The network structure of our CBD-RDN method for denoising and interpolation.

The network structure of our CBD-RDN method for denoising and interpolation... Available to Purchase

Published: 16 June 2022
Figure 1. The network structure of our CBD-RDN method for denoising and interpolation.
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Spectra of the denoised results using the CBD-RDN method with Ricker wavelets {21,21.9,…,30  Hz} and the Ricker wavelet 10  Hz as the source of each network’s training data.

Spectra of the denoised results using the CBD-RDN method with Ricker wavele... Available to Purchase

Published: 16 June 2022
Figure 25. Spectra of the denoised results using the CBD-RDN method with Ricker wavelets { 21 , 21.9 , … , 30    Hz } and the Ricker wavelet 10    Hz as the source of each network’s training data.
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Gaussian noise removal of the synthetic Marmousi data with/without skip connections. (a and b) Noisy data (S/N = 10.12) and clean data with Ricker wavelet, (c) the denoised result by the CBD-RDN method without skip connections (S/N = 21.98), and (d) the denoised result by the CBD-RDN method with skip connections (S/N = 23.57).

Gaussian noise removal of the synthetic Marmousi data with/without skip con... Available to Purchase

Published: 16 June 2022
Figure 23. Gaussian noise removal of the synthetic Marmousi data with/without skip connections. (a and b) Noisy data (S/N = 10.12) and clean data with Ricker wavelet, (c) the denoised result by the CBD-RDN method without skip connections (S/N = 21.98), and (d) the denoised result by the CBD-RDN
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Gaussian noise removal of the synthetic Marmousi data with/without the noise estimation module. (a and b) Noisy data (S/N = 10.12) and clean data with Ricker wavelet, (c) the denoised result by the CBD-RDN method without noise estimation module (S/N = 21.72), and (d) the denoised result by the CBD-RDN method with noise estimation module (S/N = 23.57).

Gaussian noise removal of the synthetic Marmousi data with/without the nois... Available to Purchase

Published: 16 June 2022
Figure 19. Gaussian noise removal of the synthetic Marmousi data with/without the noise estimation module. (a and b) Noisy data (S/N = 10.12) and clean data with Ricker wavelet, (c) the denoised result by the CBD-RDN method without noise estimation module (S/N = 21.72), and (d) the denoised
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Spectra of the PGS field data and denoised results using the curvelet, DDAE, and CBD-RDN methods.

Spectra of the PGS field data and denoised results using the curvelet, DDAE... Available to Purchase

Published: 16 June 2022
Figure 14. Spectra of the PGS field data and denoised results using the curvelet, DDAE, and CBD-RDN methods.
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Spectra of Marmousi data with coherent noise, clean data, and denoised results using the curvelet and CBD-RDN methods.

Spectra of Marmousi data with coherent noise, clean data, and denoised resu... Available to Purchase

Published: 16 June 2022
Figure 7. Spectra of Marmousi data with coherent noise, clean data, and denoised results using the curvelet and CBD-RDN methods.
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Spectra of Marmousi data with random noise, clean data, and denoised results using the curvelets, DDAE, and CBD-RDN methods.

Spectra of Marmousi data with random noise, clean data, and denoised result... Available to Purchase

Published: 16 June 2022
Figure 5. Spectra of Marmousi data with random noise, clean data, and denoised results using the curvelets, DDAE, and CBD-RDN methods.
Journal Article

Reduction of Fe(III) in griffithite Available to Purchase

P. KOMADEL, J. MADEJOVÁ, D. A. LAIRD, Y. XIA, J. W. STUCKI
Journal: Clay Minerals
Published: 01 September 2000
Clay Minerals (2000) 35 (4): 625–634.
... in laboratory investigations to reduce Fe(III) in dioctahedral smectites (see recent reviews by Stucki et al. , 1996 , and Ernstsen et al. , 1998 ). The most effective chemical reduction method, leading to >90% reduction of Fe(III) to Fe(II) in nontronites, is the citrate-bicarbonate-dithionite (CBD...
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View PDF for article title, Reduction of Fe(III) in griffithite
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Denoising of the reflected wave in PGS field data. (a) Noisy data and (b–d) the denoised results of the field data using the curvelet, DDAE, and CBD-RDN methods, respectively.

Denoising of the reflected wave in PGS field data. (a) Noisy data and (b–d)... Available to Purchase

Published: 16 June 2022
Figure 13. Denoising of the reflected wave in PGS field data. (a) Noisy data and (b–d) the denoised results of the field data using the curvelet, DDAE, and CBD-RDN methods, respectively.
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Coherent noise removal of the synthetic Marmousi data. (a and b) Noisy data and clean data, respectively, and (c and d) the denoised results by the curvelet (S/N = 12.81) and CBD-RDN (S/N = 20.05) methods, respectively.

Coherent noise removal of the synthetic Marmousi data. (a and b) Noisy data... Available to Purchase

Published: 16 June 2022
Figure 6. Coherent noise removal of the synthetic Marmousi data. (a and b) Noisy data and clean data, respectively, and (c and d) the denoised results by the curvelet (S/N = 12.81) and CBD-RDN (S/N = 20.05) methods, respectively.
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Resolution upscaling of the diving wave in PGS field data. (a) The diving wave regularly sampled with a 90 m spatial interval and (b-f) the resolution-upscaling results of (a) using the nearest, linear, curvelet, MDRIN, and CBD-RDN methods, respectively.

Resolution upscaling of the diving wave in PGS field data. (a) The diving w... Available to Purchase

Published: 16 June 2022
Figure 16. Resolution upscaling of the diving wave in PGS field data. (a) The diving wave regularly sampled with a 90 m spatial interval and (b-f) the resolution-upscaling results of (a) using the nearest, linear, curvelet, MDRIN, and CBD-RDN methods, respectively.
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Missing traces interpolation of the synthetic Marmousi data. (a and b) The 30% missing trace data and clean data, respectively, and (c and d) the reconstructed results by the curvelet (S/N = 16.68) and CBD-RDN (S/N = 20.66) methods, respectively.

Missing traces interpolation of the synthetic Marmousi data. (a and b) The ... Available to Purchase

Published: 16 June 2022
Figure 8. Missing traces interpolation of the synthetic Marmousi data. (a and b) The 30% missing trace data and clean data, respectively, and (c and d) the reconstructed results by the curvelet (S/N = 16.68) and CBD-RDN (S/N = 20.66) methods, respectively.
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An example illustrating denoising and interpolation of the synthetic Marmousi data. (a and b) Corrupted and complete data, (c) the reconstructed result using the curvelet method (S/N = 14.12); and (d–f) denoising (step 1), missing trace interpolation (step 2), and super-resolution (step 3) of the synthetic data using the CBD-RDN method (S/N = 17.67) in a pipeline.

An example illustrating denoising and interpolation of the synthetic Marmou... Available to Purchase

Published: 16 June 2022
(step 3) of the synthetic data using the CBD-RDN method (S/N = 17.67) in a pipeline.
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Super-resolution of the synthetic data of an open data set. (a and b) Regularly sampled and complete data and (c–e) the reconstructed results using the curvelet (S/N = 23.61), MDRIN (S/N = 24.09), and CBD-RDN methods (S/N = 31.28), respectively.

Super-resolution of the synthetic data of an open data set. (a and b) Regul... Available to Purchase

Published: 16 June 2022
Figure 10. Super-resolution of the synthetic data of an open data set. (a and b) Regularly sampled and complete data and (c–e) the reconstructed results using the curvelet (S/N = 23.61), MDRIN (S/N = 24.09), and CBD-RDN methods (S/N = 31.28), respectively.
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Resolution upscaling of our denoising results of the reflected wave in PGS field data. (a) The denoised result regularly sampled with a 90 m spatial interval and (b–f) the resolution-upscaling results of (a) using the nearest, linear, curvelet, MDRIN, and CBD-RDN methods, respectively.

Resolution upscaling of our denoising results of the reflected wave in PGS ... Available to Purchase

Published: 16 June 2022
Figure 15. Resolution upscaling of our denoising results of the reflected wave in PGS field data. (a) The denoised result regularly sampled with a 90 m spatial interval and (b–f) the resolution-upscaling results of (a) using the nearest, linear, curvelet, MDRIN, and CBD-RDN methods, respectively.
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Denoising of the reflected wave in PGS field data. (a) Noisy data and (b and c) the denoised results using the CBD-RDN method with 10  Hz Ricker wavelet and {21,21.9,…,30  Hz} Ricker wavelet as the source of each network’s training data, respectively.

Denoising of the reflected wave in PGS field data. (a) Noisy data and (b an... Available to Purchase

Published: 16 June 2022
Figure 24. Denoising of the reflected wave in PGS field data. (a) Noisy data and (b and c) the denoised results using the CBD-RDN method with 10    Hz Ricker wavelet and { 21 , 21.9 , … , 30    Hz } Ricker wavelet as the source of each network’s training data
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Gaussian noise removal of the synthetic Marmousi data. (a and b) Noisy data (S/N = 10.12) and clean data and (c–e) the denoised results using the curvelet (S/N = 21.12), DDAE (S/N = 21.16), and CBD-RDN method (S/N = 23.57), respectively.

Gaussian noise removal of the synthetic Marmousi data. (a and b) Noisy data... Available to Purchase

Published: 16 June 2022
Figure 4. Gaussian noise removal of the synthetic Marmousi data. (a and b) Noisy data (S/N = 10.12) and clean data and (c–e) the denoised results using the curvelet (S/N = 21.12), DDAE (S/N = 21.16), and CBD-RDN method (S/N = 23.57), respectively.
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The f-k spectra of the reflected wave in PGS field data. (a) The f-k spectra of regularly sampled data with a 90 m spatial interval and (b–f) f-k spectra of resolution-upscaling results of (a) using the nearest, linear, curvelet, MDRIN, and CBD-RDN methods, respectively.

The f - k spectra of the reflected wave in PGS field data. (a) The f - k... Available to Purchase

Published: 16 June 2022
Figure 17. The f - k spectra of the reflected wave in PGS field data. (a) The f - k spectra of regularly sampled data with a 90 m spatial interval and (b–f)  f - k spectra of resolution-upscaling results of (a) using the nearest, linear, curvelet, MDRIN, and CBD-RDN methods, respectively.
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Super-resolution of the synthetic Marmousi data. (a and b) Regularly sampled data with a 40 m spatial interval and complete data and (c–e) the reconstructed results using the curvelet (S/N = 20.14), MDRIN (S/N = 22.00), and CBD-RDN methods (S/N = 28.28), respectively.

Super-resolution of the synthetic Marmousi data. (a and b) Regularly sample... Available to Purchase

Published: 16 June 2022
Figure 9. Super-resolution of the synthetic Marmousi data. (a and b) Regularly sampled data with a 40 m spatial interval and complete data and (c–e) the reconstructed results using the curvelet (S/N = 20.14), MDRIN (S/N = 22.00), and CBD-RDN methods (S/N = 28.28), respectively.
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The f-k spectra of the diving wave in PGS field data. (a) The f-k spectra of regularly sampled data with a 90 m spatial interval and (b–f) f-k spectra of resolution-upscaling results of (a) using the nearest, linear, curvelet, MDRIN, and CBD-RDN methods, respectively.

The f - k spectra of the diving wave in PGS field data. (a) The f - k s... Available to Purchase

Published: 16 June 2022
Figure 18. The f - k spectra of the diving wave in PGS field data. (a) The f - k spectra of regularly sampled data with a 90 m spatial interval and (b–f)  f - k spectra of resolution-upscaling results of (a) using the nearest, linear, curvelet, MDRIN, and CBD-RDN methods, respectively.