Incoherent noise attenuation via randomized CP decomposition
Bibliographic record
Abstract
Tensor algebra provides a powerful framework for multidimensional seismic data processing. A noise-free seismic volume can be represented by a low-rank tensor. Noise will increase the rank of the tensor. Hence, random noise attenuation can be attained via low-rank tensor filtering. Our filtering method adopts the CANDECOMP / PARAFAC (CP) decomposition. It decomposes N-dimensional seismic data in rank-one N-dimensional volumes. Alternating Least Squares (ALS) is adopted to compute the CP decomposition. In addition, we introduce a randomized CP decomposition to speed up the ALS algorithm. Computational time is saved by avoiding unfolding and folding large tensors. We examine the performance of the fast CP decomposition on synthetic data and a 3D real field data from the Western Canadian Basin. Presentation Date: Thursday, September 28, 2017 Start Time: 11:00 AM Location: 360A 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 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 | 0.001 | 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) | 0.001 | 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".