Distributed Acoustic Sensing and Machine Learning: Rockfall Detection at Mt. Meager, B.C.
Bibliographic record
Abstract
Summary We implement machine learning for rockfall event detection based on 24 hours of Distributed Acoustic Sensing (DAS) data, acquired at Mt. Meager British Columbia. The data from September 29, 2019 is sliced into 10 second image frames, making 8640 images, with manual classification of the first 83 minutes. Three predictive neural networks based on three distinct DAS pre-processing flows provide three sets of rockfall event detections that I compare to those that register on a co-located, 3C seismometer. Each model is successful and identifies events, we find that pre-processing has notable effects on the model predictions. Each appears to be successful, but it is difficult to identify which performs the best without large scale manual classification. In the future, we plan to improve the models by increasing the amount of training data using the already existing predictions and building in-depth statistics to understand the accuracy of our models.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".