Memory-Efficient Frequency-Domain Least-Squares RTM Using Low-Rank Green's Functions via Stochastic SVD Algorithms

Frequency-domain least-squares reverse time migration (FLSRTM) is capable of producing a high-resolution reflectivity model, provided that Green's functions can be stored in memory. Green's functions employed to compute the gradient and Born-modeled data must be calculated once and then st...

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Vydáno v:IEEE transactions on geoscience and remote sensing Ročník 63; s. 1 - 12
Hlavní autoři: Revelo, Daniel E., Pestana, Reynam C., Barrera, Diego F.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Approximation algorithms
Compressed singular value decomposition (cSVD)
Computational efficiency
Computational modeling
Computer applications
Computing time
Decomposition
fast computing time
Frequency domain analysis
frequency-domain LSRTM (FLSRTM)
Green's function
Green's function methods
Green's functions
Imaging
Least squares
low-rank Green’s function
Matrix decomposition
memory efficiency
Memory management
randomized SVD (rSVD)
Reflectance
Reflectivity
Singular value decomposition
Sparse matrices
Vectors
ISSN:0196-2892, 1558-0644
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Shrnutí:Frequency-domain least-squares reverse time migration (FLSRTM) is capable of producing a high-resolution reflectivity model, provided that Green's functions can be stored in memory. Green's functions employed to compute the gradient and Born-modeled data must be calculated once and then stored; however, their size can be large, making this storage infeasible depending on the available hardware. FLSRTM using low-rank Green's functions decomposed via singular value decomposition (SVD) can be used to alleviate this constraint. However, the SVD decomposition could result in a significant increase in computational time when dealing with large datasets and models of considerable size. To overcome this issue, we propose the FLSRTM scheme with a low-rank Green's function by exploiting randomized (rSVD) and compressed SVD (cSVD) algorithms. Green's functions can then be saved efficiently as two unitary matrices with a few dominant singular values, thus requiring little memory. Following the demonstration of the feasibility of rank reduction in Green's functions, we evaluate the proposed rSVD and cSVD FLSRTM schemes versus the reference fully stored Green's functions FLSRTM and conventional low-rank SVD FLSRTM. These evaluations are conducted using both a layered model and the modified Marmousi-2 model. Our proposed FLSRTM scheme can generate image results identical to the comparative methods while also requiring less memory than FLSRTM, which saves the complete Green's functions, and less computational time compared to the FLSRTM scheme with low-rank Green's functions via conventional SVD.
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ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2025.3533259