Resolving Northern Alaska Earthquake Sequences Using the Transportable Array and Probabilistic Location Methods
Bibliographic record
Abstract
Abstract Between 2014 and 2017, almost 200 new seismic stations were installed in Alaska and northwestern Canada as part of the EarthScope USArray Transportable Array. These stations currently provide an unprecedented capability for the detection and location of seismic events in regions with otherwise relatively sparse station coverage. Two interesting earthquake sequences in 2018 and 2019 in the northeastern Brooks Range were exceptionally well recorded because of this deployment. First is the aftershock sequence of the Mw 6.4 and Mw 6.0 Kaktovik earthquakes of 12 August 2018, the largest earthquakes recorded to date in the region. The second is the Niviak swarm, southwest of the Kaktovik sequence. Since July 2018, >4000 earthquakes between magnitudes 1 and 4.3 have been recorded across a region exceeding 5000 km2. We explore how the Bayesloc probabilistic multiple seismic-event location algorithm can better resolve features of these two sequences, exploiting the large numbers of readings that the improved station coverage provides from events down to magnitudes below 2. The Bayesloc calculations consistently move events in the Kaktovik sequence a few kilometers to the northeast, providing an almost linear east-southeast-striking southern limit to the aftershock zone. Analysis of the Bayesloc joint probability distribution of corrections to travel-time predictions indicate that anomalously fast wave propagation to the southwest is likely the most significant contribution to the seismic-event mislocation. The joint relocations are more consistent with Interferometric Synthetic Aperture Radar–inferred coseismic displacement than the network location estimates. The Bayesloc relocation of the Niviak events confirms that the earthquakes are distributed between many distinct clusters of seismicity that have clearer spatial separation following the relocation. The probabilistic relocations motivate both double-difference studies to better resolve clustered seismicity at the smallest spatial scales and systematic multiple event relocation studies to calculate structure and travel-time corrections over larger scales.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".