Vector-valued seismic data denoising via widely-linear autoregressive models
Bibliographic record
Abstract
Processing vector-valued seismic datasets is a challenging task. The data are usually composed of correlated components, which are corrupted by uncorrelated random noise. Random noise reduction is, therefore, a primary goal in its processing workflow. We seek to exploit both, the correlation between data components and the uncorrelated character of noise samples, to achieve further mitigation of random noise in multicomponent datasets. We represent the data through quaternion arrays and process it via quaternion algebra. We show how to exploit the correlation between the components of vectorvalued datasets by proposing the extension of the frequencyspace deconvolution (FXDECON) to its hypercomplex version, the QFXDECON. This rather straightforward extension does not guarantee that the complete vectorial information is taken into consideration. We also show how widely-linear models can exploit this correlation. The widely-linear scheme, named WL-QFXDECON, produces longer prediction filters which have enhanced signal preservation capabilities shown through synthetic and field vector-valued data examples. Presentation Date: Wednesday, September 18, 2019 Session Start Time: 8:30 AM Presentation Start Time: 9:20 AM Location: 304B Presentation Type: Oral
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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".