Unsupervised seismic random noise attenuation via 3D enhanced multiscale features
Bibliographic record
Abstract
ABSTRACT The effective suppression of random noise while preserving the seismic signal has consistently been a key challenge in seismic data processing. The high dimensionality and complex spatial structure of 3D seismic data often cause traditional denoising methods to fail to meet the requirements of noise suppression. To address this problem, we design a novel 3D seismic denoising method based on unsupervised deep learning, which integrates enhanced multiscale feature fusion with an efficient transformer (EMST). The basic framework of the EMST uses two main encoding-decoding paths to capture multiscale features of 3D seismic data. An attention mechanism further enhances the expression of features at different scales, which are then integrated by the feature fusion layer. Subsequently, an efficient transformer is incorporated to strengthen the model’s global modeling capability. Moreover, a Monte Carlo patch selection method is used to select representative patches for optimizing the training sets, thereby enhancing the training efficiency. Experimental results on multiple datasets indicate that the EMST improves denoising performance and reduces signal leakage compared with baseline methods.
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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".