Can tile low-rank compression live up to expectations? An application to 3D multidimensional deconvolution
Bibliographic record
Abstract
Wave-equation-based seismic processing algorithms have been developed over the years with the aim of handling the 3D, full-wavefield nature of seismic waves. Multi-Dimensional Deconvolution (MDD) is one of such algorithms, commonly used to remove overburden-related effects from up/down separated wavefields (e.g., removal of free-surface multiples from ocean-bottom data). However, MDD comes with several computational challenges; this is especially the case for its time-domain implementation, which requires repeated access to Terabyte-scale seismic datasets. In this work, we present a novel algorithmic solution that leverages the inherent data sparsity of seismic data in the frequency domain by means of tile low-rank data compression. We further rely on so-called Hilbert reordering to achieve a boost in the compressibility of the dataset under study. Tile Low-Rank Matrix Vector Multiplication (TLR-MVM) is then introduced to speed up the Multi-Dimensional Convolution (MDC) operator that lies at the core of the MDD algorithm. The presented solution is tested on a realistic 3D seismic dataset modelled from the SEG/EAGE Overthrust model, and the impact of two key parameters in tile low-rank compression algorithm, namely tile size and error accuracy, is thoroughly investigated. Inversion is finally performed using the LSQR solver with all MDC operations performed onto GPUs. On a 4 A100 cluster, successful deconvolution for single virtual source is accomplished within 2 minutes (including I/O). To conclude, the proposed algorithm is deployed onto several mainstream hardware the associated roofline performance model is presented.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".