Multicomponent microseismic data denoising by 3D shearlet transform
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT The low-magnitude microseismic signals generated by fracture initiation are generally buried in strong background noise, which complicates their interpretation. Thus, noise suppression is a significant step. We have developed an effective multicomponent, multidimensional microseismic-data denoising method by conducting a simplified polarization analysis in the 3D shearlet transform domain. The 3D shearlet transform is very competitive in dealing with multidimensional data because it captures details of signals at different scales and orientations, which benefits signal and noise separation. We have developed a novel processing strategy based on a signal-detection operator that can effectively identify signal coefficients in the shearlet domain by taking the correlation and energy distribution of 3C microseismic signals into account. We perform tests on synthetic and real data sets and determine that the proposed method can effectively remove random noise and preserve weak signals.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it