A residual dictionary learning method for footprint removal from seismic data
Bibliographic record
Abstract
We introduce a novel dictionary learning strategy for removal of footprint patterns and random noise in seismic data. To this end, we construct an augmented dictionary based solely on the atoms learned from the coherenceconstrained dictionary learning (CDL), a method that is very effective on attenuating random noise. It turns out that when seismic data is contaminated with acquisition and/or processing footprint, the atoms of the learned dictionary are contaminated by coherent noise patterns. Hence, it is necessary to carry out a morphological and/or texture attribute classification of the atoms for effective footprint removal. Instead, the method that we propose relies on an augmented dictionary that is constructed using a simple data-driven empirical mode decomposition (EMD) algorithm, which leads to a dictionary that contains signal atoms and a residual dictionary that contains footprint atoms. This avoids the use of complex statistical classifications strategies to segregate the atoms of the learned dictionary. As in CDL, the proposed method does not require the user to know or adjust the noise level or the sparsity of the solution for each data set. Further, it only requires one pass of CDL dictionary learning and is shown to produce successful transfer learning results in field data. This leads to a speed-up of the denoising processing, since random and coherent noise can be removed without calculating the augmented dictionary for each time slice of the 3D data volume. Results on field data demonstrate effective footprint removal with accurate edge preservation on time slices of 3D seismic poststack data.
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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 | 0.001 | 0.001 |
| 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".