Monitoring the 2021 M<sub>w</sub> 8.2 Alaska Earthquake by an Offshore Seismic and Fluid Pressure Observation Network and Implications for Ocean‐Crust Dynamic Coupling
Bibliographic record
Abstract
Abstract Ground shaking caused by earthquakes is accompanied by seafloor and sub‐seafloor formation fluid pressure variations in offshore areas, but there have been few collocated observations of these signals. In this work, we report seismic and high‐sampling‐rate fluid pressure records of the 2021 Mw 8.2 Alaska earthquake by the Ocean Networks Canada (ONC) NEPTUNE observatory at an epicentral distance of ∼2,200 km in the northeast Pacific Ocean. The system comprises observatory nodes in various tectonic environments, with each node including buried broadband seismometers, seafloor pressure sensors, and, at two nodes, borehole pressure sensors. Seismic and tsunami waveforms of the Mw 8.2 earthquake were documented in detail. Seismic seafloor pressure variations (Psf) were dominated by Rayleigh waves of periods between 5 and 50 s, with peak amplitudes of 3–4 kPa at most sites. Waveform similarity and the linear scaling between Psf and vertical ground acceleration indicate forced acceleration of the water column being dominant in governing Psf during long‐period surface‐wave arrivals, with an additional component of elastic oscillation occurring at higher frequencies (>0.1 Hz) causing extra pressure signals. Analysis of formation pressure variations due to various types of ocean loading of distinctly different frequencies (e.g., tides, tsunami, and infragravity waves) shows stable one‐dimensional vertical loading efficiencies that depend on lithology at each borehole site, with loading response being strongly influenced by the presence of free gas at shallow depths within the Cascadia accretionary prism. Inter‐site comparisons of seismic and seafloor pressure waveforms demonstrate a key role of sediment thickness in the amplification of surface wave amplitudes.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".