Microseismic and seismic denoising via ensemble empirical mode decomposition and adaptive thresholding
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT Random and coherent noise exists in microseismic and seismic data, and suppressing noise is a crucial step in seismic processing. We have developed a novel seismic denoising method, based on ensemble empirical mode decomposition (EEMD) combined with adaptive thresholding. A signal was decomposed into individual components called intrinsic mode functions (IMFs). Each decomposed signal was then compared with those IMFs resulting from a white-noise realization to determine if the original signal contained structural features or white noise only. A thresholding scheme then removed all nonstructured portions. Our scheme is very flexible, and it is applicable in a variety of domains or in a diverse set of data. For instance, it can serve as an alternative for random noise removal by band-pass filtering in the time domain or spatial prediction filtering in the frequency-offset domain to enhance the lateral coherence of seismic sections. We have determined its potential for microseismic and reflection seismic denoising by comparing its performance on synthetic and field data using a variety of methods including band-pass filtering, basis pursuit denoising, frequency-offset deconvolution, and frequency-offset empirical mode decomposition.
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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 | 0.000 | 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 it