Using Seafloor Geodesy to Detect Vertical Deformation at the Hikurangi Subduction Zone: Insights From Self‐Calibrating Pressure Sensors and Ocean General Circulation Models
Bibliographic record
Abstract
Abstract Seafloor pressure sensor data is emerging as a promising approach to resolve vertical displacement of the seafloor in the offshore reaches of subduction zones, particularly in response to slow slip events (SSEs), although such signals are challenging to resolve due to sensor drift and oceanographic signals. Constraining offshore SSE slip distribution is of key importance to understanding earthquake and tsunami hazards posed by subduction zones. We processed seafloor pressure data from January to October 2019 acquired at the Hikurangi subduction zone, offshore New Zealand, to estimate vertical displacement associated with a large SSE that occurred beneath the seafloor array. The experiment included three self‐calibrating sensors designed to remove sensor drift, which, together with ocean general circulation models, were essential to the identification and correction of long‐period ocean variability remaining in the data after applying traditional processing techniques. We estimate that long‐period oceanographic signals that were not synchronous between pressure sensors and reference sites influenced our inferred displacements by 0.3–2.6 cm, suggesting that regionally deployed reference sites alone may not provide sufficient ocean noise correction. After incorporating long‐period ocean variability corrections into the processing, we calculate 1.0–3.3 cm of uplift during the SSE offshore Gisborne at northern Hikurangi, and 1.1–2.7 cm of uplift offshore the Hawke's Bay area at central Hikurangi. Some Hawke Bay displacements detected by pressure sensors near the trench were delayed by 6 weeks compared to the timing of slip onset detected by onshore Global Navigation Satellite System sites, suggesting updip migration of the SSE.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| 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".