3D Marchenko applications: implementation and examples

Brackenhoff, Joeri; Thorbecke, Jan; Meles, Giovanni Angelo; Koehne, Victor; Wapenaar, Kees

doi:10.1111/1365-2478.13151

Cited by 10 publications

(6 citation statements)

References 48 publications

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“…To this end, Ravasi and Vasconcelos (2021) recently reviewed the challenges associated with such a scenario and proposed a CPU-based distributed implementation that reduces the I/O burden of prior implementations and can perform equally well for both the forward and adjoint. Alternatively, Brackenhoff et al (2021) proposed to use compression techniques, such as the ZFP algorithm (Lindstrom, 2014), to reduce the size of the kernel operator. However, such an approach does not allow matrix-vector operations to be directly applied using the compressed matrix without the need for decompression.…”

Section: Related Workmentioning

confidence: 99%

High performance computing seismic redatuming by inversion with algebraic compression and multiple precisions

Hong,

Ltaief,

Ravasi

et al. 2024

The International Journal of High Performance Computing Applica

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We present a high-performance implementation of Seismic Redatuming by Inversion (SRI), which combines algebraic compression with mixed-precision (MP) computations. Seismic redatuming entails the repositioning of seismic data recorded at the surface of the Earth to a subsurface level closer to where reflections have originated. Marchenko-based redatuming is the best-in-class SRI method from a theoretical point of view because it can accurately handle full seismic wavefields with minimal data pre-processing. However, it requires intensive data movement, a large memory footprint, and extensive computations due to the need for manipulating large, formally dense, complex-valued frequency matrices in the bandwidth of the recorded seismic data. More precisely, at the heart of Marchenko modeling operator lies a Multi-Dimensional Convolution (MDC) operator, which requires the application of repeated complex-valued Matrix-Vector Multiplications (MVMs) with chosen set of seismic frequency matrices. SRI is deemed to be too time-consuming for realistic domain sizes and therefore industrially impractical. To alleviate these computational bottlenecks, we first improve the memory footprint of the MDC operator by using Tile Low-Rank Matrix-Vector Multiplications (TLR-MVMs); this exploits the low-rank nature of seismic recordings in the frequency domain, used as the kernel of the MDC modeling operator. MP computations are further introduced to increase the arithmetic intensity of the operators. We validate the numerical robustness of our synergistic approach on a benchmark 3D synthetic seismic dataset and demonstrate its use on various hardware architectures. We develop several FP32/FP16/INT8 MP TLR-MVM implementations with a limited and controllable impact on the overall accuracy of application. We achieve up to 63X memory footprint reduction and 12.5X performance speedup against the traditional dense MVM kernel on a single NVIDIA A100 GPU. Moreover, linear scalability is attained when deploying our implementation on eight A100 GPUs. Our implementations of the MDC operator integrate with the open-source PyLops framework for large-scale, matrix-free inverse problems.

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Section: Related Workmentioning

confidence: 99%

High performance computing seismic redatuming by inversion with algebraic compression and multiple precisions

Hong,

Ltaief,

Ravasi

et al. 2024

The International Journal of High Performance Computing Applica

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“…The tildes represent plane-wave quantities for focusing functions and Green's functions. The plane-wave focusing functions (Wapenaar et al, 2021;Brackenhoff et al, 2022) are defined by the following integration…”

Section: Theorymentioning

confidence: 99%

Design, implementation and application of the Marchenko Plane-wave algorithm

Thorbecke¹,

Almobarak²,

IJsseldijk³

et al. 2023

Preprint

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The Marchenko algorithm can eliminate internal multiples in seismic reflection data. To achieve this a set of coupled equations with four unknowns is solved. These coupled equations are separated into a set of two equations with two unknowns by means of a time-window. The two unknown focusing functions can be solved by an iterative or direct method. These focusing functions, when applied to the reflection data, create virtual point-sources inside the medium. Combining individual virtual point-sources into a plane-wave leads to an efficient computation of images without internal multiples. To use the Marchenko algorithm with plane-wave focusing functions, the time-window that separates the unknowns must be adapted. In this paper the design of the plane-wave Marchenko algorithm is explained and illustrated with numerically modeled and measured reflection data.

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“…Further, we have chosen to demonstrate the validity of our workflow for 2D wave propagation only. Various publications have emerged recently on the implementation of the Marchenko equation for 3D wave propagation problems [50], [51], [52]. Building on these developments, we prospect that a 3D implementation of the auxiliary equation should also be feasible.…”

Section: Application To Inverse Source Problemsmentioning

confidence: 99%

On the retrieval of forward-scattered waveforms from acoustic reflection and transmission data with the Marchenko equation

Neut¹,

Brackenhoff²,

Meles³

et al. 2022

Preprint

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A Green's function in an acoustic medium can be retrieved from reflection data by solving a multidimensional Marchenko equation. This procedure requires a-priori knowledge of the initial focusing function, which can be interpreted as the inverse of a transmitted wavefield as it would propagate through the medium, excluding (multiply) reflected waveforms. In practice, the initial focusing function is often replaced by a time-reversed direct wave, which is computed with help of a macro velocity model. Green's functions that are retrieved under this (direct-wave) approximation typically lack forward-scattered waveforms and their associated multiple reflections. We examine whether this problem can be mitigated by incorporating transmission data. Based on these transmission data, we derive an auxiliary equation for the forward-scattered components of the initial focusing function. We demonstrate that this equation can be solved in an acoustic medium with mass density contrast and constant propagation velocity. By solving the auxiliary and Marchenko equation successively, we can include forward-scattered waveforms in our Green's function estimates, as we demonstrate with a numerical example.

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3D Marchenko applications: implementation and examples

Cited by 10 publications

References 48 publications

High performance computing seismic redatuming by inversion with algebraic compression and multiple precisions

High performance computing seismic redatuming by inversion with algebraic compression and multiple precisions

Design, implementation and application of the Marchenko Plane-wave algorithm

On the retrieval of forward-scattered waveforms from acoustic reflection and transmission data with the Marchenko equation

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