Iterative deblending using the POCS algorithm in the approximate flattened domain

Abstract We proposed an improved method to eliminate the interference generated by simultaneous-source acquisition, which can help shorten the acquisition period and improve the quality of seismic data. An iterative mathematical framework is devised, which uses the projection onto convex sets algorithm to estimate the blending noise subtracted from the pseudo-deblended data to separate the blended data in an iterative way. Differently to the conventional method using the coherent-promoting operator only based on the curvelet transform, we combine the curvelet transform and the approximate flattened operator (AFO) to improve the deblended result, which can flatten seismic events approximately to preserve the details of useful signals. This is the first time that the AFO and the curvelet transform are combined to enhance the effect of the coherent-promoting operator and improve the performance of deblending. To display the advantages of the improved method, we use both simulated synthetic data and field data examples to compare and analyse the deblended results using our method and the conventional method, and confirm that the improved method can perform better.

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Trace interpolation for irregularly sampled seismic data using curvelet-transform-based projection onto convex sets algorithm in the frequency–wavenumber domain

Journal of Applied Geophysics ◽

10.1016/j.jappgeo.2015.04.007 ◽

2015 ◽

Vol 118 ◽

pp. 1-14 ◽

Cited By ~ 9

Author(s):

Bona Kim ◽

Soocheol Jeong ◽

Joongmoo Byun

Keyword(s):

Seismic Data ◽

Convex Sets ◽

Curvelet Transform ◽

Wavenumber Domain ◽

Projection Onto Convex Sets

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Remote Sensing Images Upsampling Based on Projection onto Convex Sets and Complex Wavelet Packet Transfer

Chinese Journal of Computers ◽

10.3724/sp.j.1016.2011.00482 ◽

2011 ◽

Vol 34 (3) ◽

pp. 482-488 ◽

Cited By ~ 1

Author(s):

Yan ZHANG ◽

Xian-Ying LI ◽

Yi-Yun MAN

Keyword(s):

Remote Sensing ◽

Convex Sets ◽

Wavelet Packet ◽

Remote Sensing Images ◽

Projection Onto Convex Sets ◽

Complex Wavelet

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Adaptability of Ultrasonic Lamb Wave Touchscreen to the Variations in Touch Force and Touch Area

Sensors ◽

10.3390/s21051736 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1736

Author(s):

Zengchong Yang ◽

Xiucheng Liu ◽

Bin Wu ◽

Ren Liu

Keyword(s):

Lamb Wave ◽

Weight Coefficient ◽

The Self ◽

Reference Database ◽

Learning Method ◽

Improved Method ◽

Large Area ◽

Localization Model ◽

Reference Databases ◽

First Time

Previous studies on Lamb wave touchscreen (LWT) were carried out based on the assumption that the unknown touch had the consistent parameters with acoustic fingerprints in the reference database. The adaptability of LWT to the variations in touch force and touch area was investigated in this study for the first time. The automatic collection of the databases of acoustic fingerprints was realized with an experimental prototype of LWT employing three pairs of transmitter–receivers. The self-adaptive updated weight coefficient of the used transmitter–receiver pairs was employed to successfully improve the accuracy of the localization model established based on a learning method. The performance of the improved method in locating single- and two-touch actions with the reference database of different parameters was carefully evaluated. The robustness of the LWT to the variation of the touch force varied with the touch area. Moreover, it was feasible to locate touch actions of large area with reference databases of small touch areas as long as the unknown touch and the reference databases met the condition of equivalent averaged stress.

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Efficacy of compressed sodium chloride (CSC) against E. coli and Candida auris in minutes and methods improvement for testing

Scientific Reports ◽

10.1038/s41598-020-79212-2 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Lan N. Truong ◽

Brayden D. Whitlock

Keyword(s):

Sodium Chloride ◽

Improved Method ◽

Candida Auris ◽

Antimicrobial Surfaces ◽

E Coli ◽

Route Of Transmission ◽

The World ◽

Short Time ◽

First Time ◽

Time Point

AbstractControlling infections has become one of the biggest problems in the world, whether measured in lives lost or money spent. This is worsening as pathogens continue becoming resistant to therapeutics. Antimicrobial surfaces are one strategy being investigated in an attempt to decrease the spread of infections through the most common route of transmission: surfaces, including hands. Regulators have chosen two hours as the time point at which efficacy should be measured. The objectives of this study were to characterize the new antimicrobial surface compressed sodium chloride (CSC) so that its action may be understood at timepoints more relevant to real-time infection control, under two minutes; to develop a sensitive method to test efficacy at short time points; and to investigate antifungal properties for the first time. E. coli and Candida auris are added to surfaces, and the surfaces are monitored by contact plate, or by washing into collection vats. An improved method of testing antimicrobial efficacy is reported. Antimicrobial CSC achieves at least 99.9% reduction of E. coli in the first two minutes of contact, and at least 99% reduction of C. auris in one minute.

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Deblending by direct inversion

Geophysics ◽

10.1190/geo2011-0497.1 ◽

2012 ◽

Vol 77 (3) ◽

pp. A9-A12 ◽

Cited By ~ 34

Author(s):

Kees Wapenaar ◽

Joost van der Neut ◽

Jan Thorbecke

Keyword(s):

Inverse Problem ◽

Seismic Data ◽

Iterative Procedure ◽

Inversion Process ◽

Direct Inversion ◽

Source Data ◽

Band Limited ◽

Blended Data ◽

Simultaneous Source ◽

Additional Constraints

Deblending of simultaneous-source data is usually considered to be an underdetermined inverse problem, which can be solved by an iterative procedure, assuming additional constraints like sparsity and coherency. By exploiting the fact that seismic data are spatially band-limited, deblending of densely sampled sources can be carried out as a direct inversion process without imposing these constraints. We applied the method with numerically modeled data and it suppressed the crosstalk well, when the blended data consisted of responses to adjacent, densely sampled sources.

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Superresolution of passive millimeter-wave images using a combined maximum-likelihood optimization and projection-onto-convex-sets approach

10.1117/12.438132 ◽

2001 ◽

Cited By ~ 8

Author(s):

Malur K. Sundareshan ◽

Supratik Bhattacharjee

Keyword(s):

Maximum Likelihood ◽

Millimeter Wave ◽

Convex Sets ◽

Projection Onto Convex Sets

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Normalized shaping regularization for robust separation of blended data

Geophysics ◽

10.1190/geo2018-0557.1 ◽

2019 ◽

Vol 84 (5) ◽

pp. V281-V293 ◽

Cited By ~ 1

Author(s):

Qiang Zhao ◽

Qizhen Du ◽

Xufei Gong ◽

Xiangyang Li ◽

Liyun Fu ◽

...

Keyword(s):

Cost Savings ◽

High Amplitude ◽

Data Consistency ◽

Common Source ◽

Ocean Bottom ◽

Special Cases ◽

Non Gaussian ◽

Blended Data ◽

Power Line Noise ◽

Simultaneous Source

Simultaneous source acquisition has attracted more and more attention from geophysicists because of its cost savings, whereas it also brings some challenges that have never been addressed before. Deblending of simultaneous source data is usually considered as an underdetermined inverse problem, which can be effectively solved with a least-squares (LS) iterative procedure between data consistency ([Formula: see text]-norm) and regularization ([Formula: see text]-norm or [Formula: see text]-norm). However, when it comes to abnormal noise that follows non-Gaussian distribution and possesses high-amplitude features (e.g., erratic noise, swell noise, and power line noise), the [Formula: see text]-norm is a nonrobust statistic that can easily lead to suboptimal deblended results. Although abnormal noise can be attenuated in the common source domain at first, it is still challenging to apply a coherency-based filter due to the sparse receiver or crossline sampling, e.g., that commonly found in ocean bottom node (OBN) acquisition. To address this problem, we have developed a normalized shaping regularization to make the inversion-based deblending approach robust for the separation of blended data when abnormal noise exists. Its robustness comes from the normalized shaping operator defined by the confidence interval of normal distribution, which minimizes the abnormal risk to a normal level to satisfy the assumption of LS shaping regularization. In special cases, the proposed approach will revert to the classic LS shaping regularization once the normalized coefficient is large enough. Experimental results on synthetic and field data indicate that the proposed method can effectively restore the separated records from blended data at essentially the same convergence rate as the LS shaping regularization for the abnormal noise-free scenario, but it can obtain better deblending performance and less energy leakage when abnormal noise exists.

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