Seismic Data Archive in the United Kingdom to support Nuclear Test Monitoring
Bibliographic record
Abstract
In 1961, AWE Blacknest became the home of Forensic Seismology in the UK, with the aim of developing and maintaining a capability to provide seismological advice to the UK government. During the 1960s the group set up a seismometer array in Scotland and worked with host countries to set up arrays in Canada, Australia (now both IMS stations), India and Brazil. AWE Blacknest has continuous data archives from these sites dating back to 1961 on a mix of paper helicorder records (seismic and infrasound traces), analogue FM-encoded tape and digital tape. From 2006-15 Blacknest developed and ran an extensive programme to overcome the ageing issues presented by vintage magnetic media condition and formats, and recovered and digitised the tapes, putting the continuous data on to modern computer storage systems. Since the 1990s data have been directly recorded to digital storage systems. Historically only events of interest, including data recorded from suspected nuclear explosions, were extracted, and Blacknest is running a programme of analysing these events and preparing the data and analysis in a form for public release. I will present on the work undertaken to develop the programme, data recovery and digitization methods from magnetic media, and the modern storage systems Blacknest use for serving seismic data. This will also include analysis work and the data inventories that Blacknest is making available.UK Ministry of Defence © Crown Owned Copyright 2024/AWE
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.058 | 0.035 |
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".