Microseismic signal reconstruction by block matching with a novel matching criterion
Bibliographic record
Abstract
Noise in surface microseismic data usually has complicated features, signal reconstruction from various types of noise is very challenging for existing reconstruction techniques. In this paper, we propose to use a trilateral weighted sparse coding scheme within the block matching framework for microseismic signal reconstruction. Block matching requires identification of similar patches in the data. This becomes more challenging for low quality data. We overcome this obstacle by using Zernike moments whose amplitudes are rotation invariant and less sensitive to noise. This in turn benefits the subsequent filtering stage by trilateral weighted sparse coding. We introduce two weight matrices to characterize the complex noise properties, and a third weight matrix to characterize the sparsity priors of signal. The improved sparse coding model is solved by the alternating direction method of multipliers. Tests on synthetic and field surface microseismic datasets show that the proposed strategy is very effective for microseismic signal recovery from the data contain complex noise interference. Presentation Date: Wednesday, October 14, 2020 Session Start Time: 1:50 PM Presentation Time: 3:30 PM Location: 360C Presentation Type: Oral
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".