Microseismic event or noise: Automatic classification with convolutional neural networks
Bibliographic record
Abstract
Microseismic monitoring is a crucial element to understanding hydraulic fracturing operations prior to oil and gas production. One of the more tedious quality control (QC) measures that must often be performed following a microseismic processing workflow is a visual inspection of seismic data to determine whether the data contain microseismic events or only noise. The manual nature of these inspections can take many weeks, sometimes over a month, to perform for one geophysicist. Automated approaches usually use a short-term-average long-termaverage (STA/LTA) ratio, but end up picking false positives on noisy data. We propose using a supervised deep learning algorithm, a convolutional neural network (CNN), to automatically classify microseismic events from noise. Using our deep learning approach, we show that the time for QC can be reduced from weeks to hours with high accuracy. Presentation Date: Monday, October 12, 2020 Session Start Time: 1:50 PM Presentation Time: 3:30 PM Location: Poster Station 1 Presentation Type: Poster
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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".