Infrasound detection of avalanches: operational experience from 28 combined winter seasons and future developments
Bibliographic record
Abstract
We present an overview of multiple verification campaigns performed to evaluate the performance of and experience with IDA® (Infrasound Detection of Avalanches) operational systems in Austria, Switzerland, Canada, Norway and the USA. This work focuses on operationally relevant facts and recommendations for the design of infrasound systems. The comprehensive dataset consists of 28 combined operational winter seasons at 10 different locations, covering a wide range of avalanche sizes and types, snow climates (stratigraphy and snow depth), topographies and site-specific characteristics. The IDA® systems automatically detected natural avalanches, artillery gun shots and detonations as well as explosions from different remote avalanche control systems. Results show that the operational reliability of IDA® is limited to avalanches of size class > 2.5 (corresponding to ~ 500 m of run-out distance and 5 ha), both dry and wet, within a distance of 3-4 km from the array, with a probability of detection (POD) between 40 and 90% and a false alert ratio (FAR) between 0 and 20%. The POD increases with size and decreases with distance. Differences in performance are mainly related to site-specific characteristics. Site-specific calibration of the automatic algorithm as well as tuning of the thresholds is a key factor for the performance optimization. The presence of local terrain features and complex topography can limit the monitoring of certain avalanche paths. Preliminary results suggest the use of multiple arrays and adapted algorithms can be an effective solution to mitigate this limitation. Wind noise, ice layers or a dense snowpack can significantly reduce the detection capability and in extreme cases render the system inoperative. However, a detailed design study, optimized site selection, properly installation solutions, hardware robustness improvements and a clear definition of the operational requirements of the local avalanche control team can help minimizee these limitations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".