Mode-grouped tensor decomposition for attenuating migration artifacts in wave-equation migration and inversion
Bibliographic record
Abstract
Abstract Seismic images reconstructed by migration in the angle domain naturally form multilinear arrays, or tensors, where each mode corresponds to a spatial or angular dimension. This structure enables the application of tensor decomposition techniques to address challenges such as artifact suppression and preconditioning for inversion. Migration artifacts in angle-domain common-image gathers (ADCIGs), resulting from limited acquisition geometry, compromise the reliability of amplitude-versus-angle analysis and may lead to structural interpretation errors. A mode-grouped tensor decomposition exploits the low-rank structure inherent in ADCIGs to mitigate these issues. The method reorganizes spatial dimensions with shared physical meaning into a unified tensor, yielding a compact and physically interpretable representation. The decomposition is solved via alternating minimization of linear least-squares subproblems. A smoothing constraint is imposed along the angle dimension to enhance continuity. Each subproblem is efficiently handled using conjugate gradient iterations. Tests on synthetic and field datasets confirm that the proposed method attenuates migration artifacts more effectively than traditional approaches, such as Tucker decomposition, while better preserving geological continuity. When incorporated as a projection operator in linearized waveform inversion, the decomposition accelerates convergence and leads to higher-quality inverted images.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".