Inverse problem for Goupillaud‐layered earth model and dynamic deconvolution

Bardan, V.; Robinson, Enders A.

doi:10.1111/1365-2478.12676

Cited by 4 publications

(4 citation statements)

References 12 publications

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“…The fact that the Marchenko equation and the Kunetz theory are connected was shown by Ware and Aki (1969) and recently illustrated in detail by Bardan and Robinson (2018). Slob et al (2020) unified two bodies of thought.…”

Section: Introductionmentioning

confidence: 85%

Unified elimination of 1D acoustic multiple reflection

Slob

Zhang

2021

Geophysical Prospecting

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Migration, velocity and amplitude analysis are the employed processing steps to find the desired subsurface information from seismic reflection data. The presence of freesurface and internal multiples can mask the primary reflections for which many processing methods are built. The ability to separate primary from multiple reflections is desirable. Connecting Marchenko theory with classical one-dimensional inversion methods allows to understand the process of multiple reflection elimination as a datafiltering process. The filter is a fundamental wave field, defined as a pressure and particle velocity that satisfy the wave equation. The fundamental wave field does not depend on the presence or absence of free-surface multiples in the data. The backbone of the filtering process is that the fundamental wave field is computed from the measured pressure and particle velocity without additional information. Two different multiples-free datasets are obtained: either directly from the fundamental wave field or by applying the fundamental wave field to the data. In addition, the known schemes for Marchenko multiple elimination follow from the main equation. Numerical examples show that source and receiver ghosts, free-surface and internal multiples can be removed simultaneously using a conjugate gradient scheme. The advantage of the main equation is that the source wavelet does not need to be known and no preprocessing is required. The fact that the reflection coefficients can be obtained is an interesting feature that could lead to improved amplitude analysis and inversion than would be possible with other processing methods.

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Section: Introductionmentioning

confidence: 85%

Unified elimination of 1D acoustic multiple reflection

Slob

Zhang

2021

Geophysical Prospecting

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“…In order to facilitate the calculation, the physical structure of the nasal cavity needs to be simplified. The schematic diagram of acoustic wave propagation in a simplified nasal cavity is shown in Figure 7 [24].…”

Section: The Nasal Cavity Three-dimensional Structure Hierarchical Re...mentioning

confidence: 99%

“…As shown on the right side of Figure 8, the reflectance of the reflected wave is −r i and the transmittance is −t i . The transmittance and reflectance satisfy Equation (4) [24,25]:…”

Section: The Nasal Cavity Three-dimensional Structure Hierarchical Re...mentioning

confidence: 99%

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Three-Dimensional Structure Reconstruction System of Nasal Cavity Based on a Short-Source Acoustic Tube

Lian,

Ma,

Gao

et al. 2023

Applied Sciences

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The nasal cavity three-dimensional structure reconstruction is significant for the diagnosis and treatment of nasal and nasopharynx diseases. Currently, most systems for nasal cavity three-dimensional structure reconstruction are based on a long-source acoustic tube. However, the long-source acoustic tube has the weaknesses of low portability and high cost. To address these issues, a nasal cavity three-dimensional structure reconstruction system based on a short-source acoustic tube was designed in this research. The designed system comprises the lower computer of a nasal acoustic signal acquisition device and the upper computer of nasal acoustic signal analysis software. The lower computer of a nasal acoustic signal acquisition device consists of a signal processing unit, a detection unit, a microcontroller, and a USB communication interface. It is used to collect the original acoustic signal in the nasal cavity. The upper computer of nasal acoustic signal analysis software consists of the nasal cavity acoustic signal preprocessing method and the nasal cavity three-dimensional structure hierarchical reconstruction method. The nasal cavity acoustic signal preprocessing method is used to eliminate the DC offset component and suppress the high-frequency noise. Then, the nasal cavity three-dimensional structure hierarchical reconstruction method is used to establish the relationship between the cross-sectional area of the nasal cavity and the depth of the nasal cavity. In order to verify the effectiveness of the system designed in this research, the straight tube model and the simulated nasal cavity model were selected as test subjects, and the depth–cross-sectional area was selected as the evaluation indicator of the accuracy of the nasal cavity three-dimensional structure reconstruction result. The experimental results show that the root mean squared error and the mean relative error of the depth–cross-sectional area were reduced from 0.6284 cm2 to 0.0201 cm2 and 47.6467% to 1.8248%, respectively, under the straight tube model by using the nasal cavity acoustic signal preprocessing method. The root mean squared error and the mean relative error of the depth–cross-sectional area were reduced from 4.7730 cm2 to 0.1510 cm2 and 107.8128% to 3.3096%, respectively, under the simulated nasal cavity model. Meanwhile, this system can increase the effective measurement depth of the nasal cavity from 7 cm to 13 cm based on the preprocessing method. The results demonstrated that the designed system can not only reconstruct and visualize a nasal cavity three-dimensional structure with high precision but also generate comprehensive test reports. Therefore, this system can provide a basis for further auxiliary diagnosis of the disease and facilitate doctors in explaining the physiological structure of the nasal cavity to patients.

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Tutorial: unified 1D inversion of the acoustic reflection response

Slob

Wapenaar

Treitel³

2020

Geophysical Prospecting

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Acoustic inversion in one‐dimension gives impedance as a function of travel time. Inverting the reflection response is a linear problem. Recursive methods, from top to bottom or vice versa, are known and use a fundamental wave field that is computed from the reflection response. An integral over the solution to the Marchenko equation, on the other hand, retrieves the impedance at any vertical travel time instant. It is a non‐recursive method, but requires the zero‐frequency value of the reflection response. These methods use the same fundamental wave field in different ways. Combining the two methods leads to a non‐recursive scheme that works with finite‐frequency bandwidth. This can be used for target‐oriented inversion. When a reflection response is available along a line over a horizontally layered medium, the thickness and wave velocity of any layer can be obtained together with the velocity of an adjacent layer and the density ratio of the two layers. Statistical analysis over 1000 noise realizations shows that the forward recursive method and the Marchenko‐type method perform well on computed noisy data.

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Inverse problem for Goupillaud‐layered earth model and dynamic deconvolution

Cited by 4 publications

References 12 publications

Unified elimination of 1D acoustic multiple reflection

Unified elimination of 1D acoustic multiple reflection

Three-Dimensional Structure Reconstruction System of Nasal Cavity Based on a Short-Source Acoustic Tube

Tutorial: unified 1D inversion of the acoustic reflection response

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