Wavefield Decomposition by 3-D/3-C Inversion Process in Tau-P Domain

2003 ◽  
Author(s):  
J. Blanco ◽  
G. Canadas
Keyword(s):  
Inversion Process ◽  
P Domain ◽  
Wavefield Decomposition

Least-squares reverse time migration using analytic-signal-based wavefield decomposition

Geophysics ◽  
2019 ◽  
Vol 84 (3) ◽  
pp. S149-S157 ◽  
Author(s):  
Young Seo Kim ◽  
Ali Almomin ◽  
Woodon Jeong ◽  
Constantine Tsingas
Keyword(s):  
Least Squares ◽  
Fourier Transforms ◽  
Early Stage ◽  
Vertical Direction ◽  
Analytic Signal ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Inversion Process ◽  
Time Migration ◽  
Wavefield Decomposition

An intrinsic problem during migration and imaging of seismic wavefields using the two-way wave equation is the crosstalk interference between the up/down propagation of the corresponding source and receiver wavefields. To mitigate this crosstalk, the downgoing source and upgoing receiver wavefield imaging condition (IC) is adopted at an early stage of the inversion process, improving convergence and obtaining cleaner reflection images. A wavefield decomposition methodology can also be incorporated into a least-squares reverse time migration (LSRTM) algorithm. The separation of wavefields based on the propagation direction in the early iterations of LSRTM is to reduce interference noise during the inversion process given that the IC considers only primary reflections. Wavefields decomposed with respect to the vertical direction can be easily obtained by Fourier transforms on the time and vertical axes; however, they usually require significantly higher computational effort especially for 3D applications. Vertical wavefield decomposition by a complex-valued analytic signal is an alternative method implemented by the Hilbert transform, which can be conducted by 1D Fourier transform only on the vertical axis. An LSRTM algorithm adopting this decomposition method has a disadvantage in that it requires two additional wave modelings at each iteration. However, by adapting the deprimary IC into LSRTM, only one more modeling is additionally required in the backward wavefield propagation as compared with conventional LSRTM. Our LSRTM using wavefield decomposition has the ability to produce broader band reflectivity images than conventional LSRTM. This is demonstrated with numerical examples using synthetic and real data resulting artifact-free migration results and broadband reflectivity images.

Microporous Membranes from an Insoluble Polyimide

1995 ◽  
Vol 60 (10) ◽  
pp. 1741-1746
Author(s):  
Jan Schauer ◽  
Miroslav Marek
Keyword(s):  
Acetic Anhydride ◽  
Phase Inversion ◽  
Amic Acid ◽  
Microporous Membranes ◽  
Inversion Process ◽  
Phase Inversion Process ◽  
Ultrafiltration Process

Poly(amic acid) prepared from 3,3',4,4'-benzophenonetetracarboxylic dianhydride and bis(4-aminophenyl) ether was used for preparation of microporous membranes by the phase inversion process. Membranes coagulated in acetic anhydride were brittle but usable for ultrafiltration. Coagulation of the poly(amic acid) in water or lower alcohols and subsequent thermal cyclocondensation led to extremely brittle polyimides, which limits their use for ultrafiltration process.

scholarly journals Low-Frequency Expansion Approach for Seismic Data Based on Compressed Sensing in Low SNR

2021 ◽  
Vol 11 (11) ◽  
pp. 5028
Author(s):  
Miaomiao Sun ◽  
Zhenchun Li ◽  
Yanli Liu ◽  
Jiao Wang ◽  
Yufei Su
Keyword(s):  
Reflection Coefficient ◽  
Compressed Sensing ◽  
Objective Function ◽  
Low Frequency ◽  
Original Data ◽  
Seismic Exploration ◽  
High Signal ◽  
Inversion Process ◽  
Frequency Information ◽  
Low Snr

Low-frequency information can reflect the basic trend of a formation, enhance the accuracy of velocity analysis and improve the imaging accuracy of deep structures in seismic exploration. However, the low-frequency information obtained by the conventional seismic acquisition method is seriously polluted by noise, which will be further lost in processing. Compressed sensing (CS) theory is used to exploit the sparsity of the reflection coefficient in the frequency domain to expand the low-frequency components reasonably, thus improving the data quality. However, the conventional CS method is greatly affected by noise, and the effective expansion of low-frequency information can only be realized in the case of a high signal-to-noise ratio (SNR). In this paper, well information is introduced into the objective function to constrain the inversion process of the estimated reflection coefficient, and then, the low-frequency component of the original data is expanded by extracting the low-frequency information of the reflection coefficient. It has been proved by model tests and actual data processing results that the objective function of estimating the reflection coefficient constrained by well logging data based on CS theory can improve the anti-noise interference ability of the inversion process and expand the low-frequency information well in the case of a low SNR.

Parametric wavefield decomposition and applications

1990 ◽  
Author(s):  
W. Scott Leaney ◽  
E. P. Schlumberger
Keyword(s):  
Wavefield Decomposition

A Three-Step Model for Submicron W/O Emulsion Formation in a Transitional-Phase Inversion Process

2015 ◽  
Vol 37 (8) ◽  
pp. 1186-1191 ◽  
Author(s):  
Xiaolong Wang ◽  
Yonghong Liu ◽  
Hang Dong ◽  
Qiang Sun ◽  
Yang Shen ◽  
...  
Keyword(s):  
Phase Inversion ◽  
Inversion Process ◽  
Step Model ◽  
Transitional Phase ◽  
Phase Inversion Process

scholarly journals Deblending by direct inversion

Geophysics ◽  
2012 ◽  
Vol 77 (3) ◽  
pp. A9-A12 ◽  
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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