Dissipative attenuation and dispersion modeling with application in Campos basin

1987 ◽  
Author(s):  
Carlos Lopo Varela ◽  
Vandemir Ferreira de Oliveira ◽  
Eduardo Lopes de Faria
Keyword(s):  
Dispersion Modeling ◽  
Campos Basin ◽  
Attenuation And Dispersion

scholarly journals Skull’s Photoacoustic Attenuation and Dispersion Modeling with Deterministic Ray-Tracing: Towards Real-Time Aberration Correction

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 345 ◽  
Author(s):  
Leila Mohammadi ◽  
Hamid Behnam ◽  
Jahan Tavakkoli ◽  
Mohammad Avanaki
Keyword(s):  
Ray Tracing ◽  
Mode Conversion ◽  
Ex Vivo ◽  
Photoacoustic Imaging ◽  
Time Shift ◽  
Dispersion Modeling ◽  
Transmission Coefficients ◽  
Time Domain Signal ◽  
Wave Transmission Coefficients ◽  
Attenuation And Dispersion

Although transcranial photoacoustic imaging has been previously investigated by several groups, there are many unknowns about the distorting effects of the skull due to the impedance mismatch between the skull and underlying layers. The current computational methods based on finite-element modeling are slow, especially in the cases where fine grids are defined for a large 3-D volume. We develop a very fast modeling/simulation framework based on deterministic ray-tracing. The framework considers a multilayer model of the medium, taking into account the frequency-dependent attenuation and dispersion effects that occur in wave reflection, refraction, and mode conversion at the skull surface. The speed of the proposed framework is evaluated. We validate the accuracy of the framework using numerical phantoms and compare its results to k-Wave simulation results. Analytical validation is also performed based on the longitudinal and shear wave transmission coefficients. We then simulated, using our method, the major skull-distorting effects including amplitude attenuation, time-domain signal broadening, and time shift, and confirmed the findings by comparing them to several ex vivo experimental results. It is expected that the proposed method speeds up modeling and quantification of skull tissue and allows the development of transcranial photoacoustic brain imaging.

Modeling of attenuation and dispersion

Geophysics ◽  
1993 ◽  
Vol 58 (8) ◽  
pp. 1167-1173 ◽  
Author(s):  
Carlos Lopo Varela ◽  
Andre L. R. Rosa ◽  
Tadeusz J. Ulrych
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Low Cost ◽  
Real Data ◽  
Dispersion Modeling ◽  
Minimum Phase ◽  
The Time Domain ◽  
Wavefield Extrapolation ◽  
Attenuation And Dispersion ◽  
Proper Solutions

At the present time, proper solutions for absorption modeling are based on wavefield extrapolation techniques which, in some instances, may be considered expensive. Two alternative, low cost, but incomplete solutions exist in the literature. The first models dispersion in the frequency domain in accordance with the Futterman dispersive relations but does not consider attenuation. The second models both attenuation and dispersion in the time domain but assumes a digital minimum‐phase formulation that results in an inadequate treatment of the dispersion. We show that this second solution can be adapted to perform attenuation and/or dispersion modeling in agreement with the Futterman attenuation‐dispersion relationships thus obviating the shortcoming mentioned above. Synthetic and real data examples are shown to illustrate the performance of the proposed algorithm.

AIR DISPERSION MODELING OF VOCs EMITTED FROM MSW LANDFILLS

2006 ◽  
Vol 5 (4) ◽  
pp. 731-741
Author(s):  
Fatih Taspinar ◽  
Ertan Durmusoglu ◽  
Aykan Karademir
Keyword(s):  
Dispersion Modeling ◽  
Air Dispersion Modeling ◽  
Air Dispersion

scholarly journals An Empirical Bayes approach for the identification of long-range chromosomal interaction from Hi-C data

2021 ◽  
Vol 20 (1) ◽  
pp. 1-15
Author(s):  
Qi Zhang ◽  
Zheng Xu ◽  
Yutong Lai
Keyword(s):  
Long Range ◽  
Latent Variables ◽  
Empirical Bayes ◽  
Genome Structure ◽  
Peak Detection ◽  
Data Matrix ◽  
Dispersion Modeling ◽  
Empirical Bayes Approach ◽  
Over Dispersion ◽  
Bayes Approach

Abstract Hi-C experiments have become very popular for studying the 3D genome structure in recent years. Identification of long-range chromosomal interaction, i.e., peak detection, is crucial for Hi-C data analysis. But it remains a challenging task due to the inherent high dimensionality, sparsity and the over-dispersion of the Hi-C count data matrix. We propose EBHiC, an empirical Bayes approach for peak detection from Hi-C data. The proposed framework provides flexible over-dispersion modeling by explicitly including the “true” interaction intensities as latent variables. To implement the proposed peak identification method (via the empirical Bayes test), we estimate the overall distributions of the observed counts semiparametrically using a Smoothed Expectation Maximization algorithm, and the empirical null based on the zero assumption. We conducted extensive simulations to validate and evaluate the performance of our proposed approach and applied it to real datasets. Our results suggest that EBHiC can identify better peaks in terms of accuracy, biological interpretability, and the consistency across biological replicates. The source code is available on Github (https://github.com/QiZhangStat/EBHiC).

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