Multichannel singular spectrum analysis denoising and reconstruction for irregular grid (I-MSSA)
Bibliographic record
Abstract
Multichannel Singular Spectrum Analysis (MSSA) allows simultaneous random noise reduction and reconstruction of multidimensional seismic data. Under ideal conditions, seismic data can be represented by low-rank matrices, but noise and missing traces affect this property. Then, MSSA iteratively applies rank-reduction algorithms to reconstruct the corrupted seismic volume. The method starts by reorganizing the samples in block Hankel matrices. Then, it applies a rank-reduction algorithm followed by antidiagonal averaging of reduced-rank block Hankel matrices to recover the signal. Finally, it implements an imputation scheme that recovers the denoised and reconstructed multidimensional seismic array. A significant shortcoming to the method is that MSSA requires input data on a regular grid. In essence, one adopts bin centered coordinates instead of using the actual coordinates of the seismic traces. Consequently, the MSSA reconstruction method does not honor exact spatial positions. We consider an inversion approach that reconstructs irregularly distributed data via MSSA. We minimize the misfit between the observed data and the reconstructed volume in the exact coordinates, subject to the low-rank constraint of the classical MSSA method. For this purpose, we propose an iterative process based on the Projected Gradient Descent algorithm on the exact data coordinates, while the MSSA filter operates on the regular output grid. We call the proposed new algorithm for irregular data I-MSSA. We explore two algorithms that use Projected Gradient Descent and compare their performance. We test the algorithms using synthetic examples. Finally, we propose an application of the I-MSSA algorithm to reconstruct a 3D prestack volume on the CMP domain, using cross-spread gathers. Presentation Date: Wednesday, October 14, 2020 Session Start Time: 8:30 AM Presentation Time: 8:55 AM Location: 360D Presentation Type: Oral
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".