Ringing mountain ranges: teleseismic signature of the interaction of high-frequency wavefields with near-source topography at the Degelen nuclear test site
Bibliographic record
Abstract
SUMMARY Over the last decade there has been an international effort to find methods to recover and digitise recordings from historical earthquakes and explosions that occurred during the 1950s through to the 1980s. Making these recordings accessible in digital format offers opportunities to study what signatures are encoded in the data, and to apply state-of-the-art techniques and methods to historical data. In this study, we employ unsupervised machine learning to cluster historical teleseismic waveforms from nuclear explosions conducted at the former USSR Degelen test site, in Kazakhstan, recorded at seismic arrays in the UK (Eskdalemuir array), Canada (Yellowknife array), Australia (Warramunga array), and India (Gauribidanur array). In particular, we use two unsupervised algorithms to cluster waveforms using shape-based clustering: kernel k-means and k-Shape. The algorithms clearly split waveforms into distinct clusters that are spatially related, even when waveform differences are subtle, and we show with local and teleseismic numerical simulations that the clusters are related to the topography. The topography at the Degelen test site has characteristic wavelengths of 2–4 km and local simulations highlight that the seismic wavefield is trapped in reverberating mountain peaks. The location of the explosion is crucial in determining which section of the mountain range reverberates, influencing the outgoing wavefield. Teleseismic waveform simulations confirm that it is this superposition of energy leaving the reverberating peaks that results in the observed teleseismic waveform differences we observe.
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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 | 0.001 |
| 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".