Data interpolation for 3D Marchenko Green’s function retrieval

Jia, Xueyi; Zhao, Yang; Snieder, Roel

doi:10.1190/segam2019-3216672.1

Cited by 8 publications

(3 citation statements)

References 17 publications

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“…Both the full dataset as well as three spatially decimated versions of the dataset are used to illustrate the robustness of these redatuming methods to spatial aliasing in the input dataset for a simple geological model. Complementing the work of Jia et al (2018b), Lomas and Curtis (2020), and Brackenho↵ et al (2020), our study shows that the Marchenko equations can also be directly inverted by means of an iterative optimization algorithm (e.g., conjugate greadient least-squares (CGLS) - Shewchuk (1994)) in three dimensions (and not only via Neumann iterations). Moreover, similar to Bracken-ho↵ et al (2019), our example shows how the estimated Green's functions can be further used for target-oriented imaging purposes also in cases where the sampling requirements are mildly violated.…”

Section: Introductionsupporting

confidence: 77%

“…The solution of the Marchenko equations for 3D datasets has so far been limited to the Neumann-type iterative scheme Jia et al, 2018b;Lomas and Curtis, 2019;Brackenho↵ et al, 2020;Staring and Wapenaar, 2020). In this paper we present the first 3D example of Marchenko redatuming where the solution is obtained using a gradient-based solver.…”

Section: Receiver-side Redatuming Via Marchenko Equationsmentioning

confidence: 99%

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An open-source framework for the implementation of large-scale integral operators with flexible, modern high-performance computing solutions: Enabling 3D Marchenko imaging by least-squares inversion

Ravasi

Vasconcelos

2021

GEOPHYSICS

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Numerical integral operators of convolution type form the basis of most wave-equation-based methods for processing and imaging of seismic data. As several of these methods require the solution of an inverse problem, multiple forward and adjoint passes of the modeling operator are generally required to converge to a satisfactory solution. This work highlights the memory requirements and computational challenges that arise when implementing such operators on 3D seismic datasets and their usage for solving large systems of integral equations. A Python framework is presented that leverages libraries for distributed storage and computing, and provides a high-level symbolic representation of linear operators. A driving goal for our work is not only to offer a widely deployable, ready-to-use high-performance computing (HPC) framework, but to demonstrate that it enables addressing research questions that are otherwise difficult to tackle. To this end, the first example of 3D full-wavefield target-oriented imaging, which comprises of two subsequent steps of seismic redatuming, is presented. The redatumed fields are estimated by means of gradient-based inversion using the full dataset as well as spatially decimated versions of the dataset as a way to investi-gate the robustness of both inverse problems to spatial aliasing in the input dataset. Our numerical example shows that when one spatial direction is finely sampled, satisfactory redatuming and imaging can be accomplished also when the sampling in other direction is coarser than a quarter of the dominant wavelength. While aliasing introduces noise in the redatumed fields, they are less sensitive to well-known spurious artefacts compared to cheaper, adjoint-based redatuming techniques. These observations are shown to hold for a relatively simple geologic structure, and while further testing is needed for more complex scenarios, we expect them to be generally valid while possibly breaking down for extreme cases

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Section: Introductionsupporting

confidence: 77%

Section: Receiver-side Redatuming Via Marchenko Equationsmentioning

confidence: 99%

An open-source framework for the implementation of large-scale integral operators with flexible, modern high-performance computing solutions: Enabling 3D Marchenko imaging by least-squares inversion

Ravasi

Vasconcelos

2021

GEOPHYSICS

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“…Most field test results are with two‐dimensional (2D) implementations on line data (Jia et al ., 2018; Staring et al ., 2018; Brackenhoff et al ., 2019). The effect of line data on 2D methods and out‐of‐plane effects as well as sampling conditions for three‐dimensional (3D) applications have been analysed (Jia et al ., 2019; Lomas and Curtis, 2020). A 3D example of approximate double focusing can be found in Staring and Wapenaar (2020).…”

Section: Introductionmentioning

confidence: 99%

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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Compressive Sensing Acquisition with Application to Marchenko Imaging

Zhang

2022

Pure Appl. Geophys.

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Data interpolation for 3D Marchenko Green’s function retrieval

Cited by 8 publications

References 17 publications

An open-source framework for the implementation of large-scale integral operators with flexible, modern high-performance computing solutions: Enabling 3D Marchenko imaging by least-squares inversion

An open-source framework for the implementation of large-scale integral operators with flexible, modern high-performance computing solutions: Enabling 3D Marchenko imaging by least-squares inversion

Unified elimination of 1D acoustic multiple reflection

Compressive Sensing Acquisition with Application to Marchenko Imaging

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