Improving sparse representation with deep learning: A workflow for separating strong background interference
Bibliographic record
Abstract
ABSTRACT Revealing hidden reservoirs that are severely shielded by strong background interference (SBI) is critical to subsequent refined interpretation. To enhance the characterization of these reservoirs, current interpretation workflows merge multiple attribute information, necessitating intensive human expertise. As an alternative, we regard SBI suppression as a signal separation problem and develop a workflow to suppress SBI by cascading a sparse representation method and deep learning. SBI has coherent morphological characteristics in seismic sections; reservoir seismic responses, such as channels and karst caves, have a narrow spatial distribution, exhibiting abrupt morphological characteristics. As their morphologies differ, we select two 2D sparse representation dictionaries to identify their individual components. Through the morphological component analysis (MCA) technique, we can obtain adequate SBI separation results. However, the MCA separation is inevitably limited because 2D dictionaries cannot adequately represent 3D structures, but 3D dictionaries are not viable due to computing constraints. As an extension, we use 3D deep learning to improve the separation results based on the 2D MCA results. Specifically, the network is fed with training samples from a region with better SBI suppression results obtained by the MCA method. After learning a direct mapping from noisy data to SBI, the network can improve the separation results and remove more SBI than the previous conventional method. Field data experiments demonstrate that our separation workflow successfully enhances reservoir structures after removing SBI.
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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".