Large-scale Marchenko imaging with distance-aware matrix reordering, tile low-rank compression, and mixed-precision computations

Matteo Ravasi, Yuxi Hong, Hatem Ltaief, David E. Keyes, David Vargas

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

A variety of wave-equation-based seismic processing algorithms rely on the repeated application of the Multi- Dimensional Convolution (MDC) operator. For large-scale 3D seismic surveys, this comes with severe computational challenges due to the sheer size of high-density, full-azimuth seismic datasets required by such algorithms. We present a three-fold solution that greatly alleviates the memory footprint and computational cost of 3D MDC by leveraging a combination of i) distance-aware matrix reordering, ii) Tile Low-Rank (TLR) matrix compression, and iii) computations in mixed floating-point precision. By applying our strategy to a 3D synthetic dataset, we show that the size of kernel matrices used in the Marchenko redatuming and Multi-Dimensional Deconvolution equations can be reduced by a factor of 34 and 6, respectively. We also introduce a TLR Matrix-Vector Multiplication (TLR-MVM) algorithm that, as a direct consequence of such compression capabilities, is consistently faster than its dense counterpart by a factor of 4.8 to 36.1 (depending on the selected hardware). As a result, the associated inverse problems can be solved at a fraction of cost in comparison to state-of- the-art implementations that require a pass through the entire data at each MDC operation. This is achieved with minimal impact on the quality of the processing outcome.
Original languageEnglish (US)
Title of host publicationSecond International Meeting for Applied Geoscience & Energy
PublisherSociety of Exploration Geophysicists and American Association of Petroleum Geologists
DOIs
StatePublished - Aug 15 2022

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