Seabed seismometers reveal duration and structure of longest runout sediment flows on Earth
Bibliographic record
Abstract
Seafloor sediment flows (turbidity currents) form some of the largest sediment accumulations on Earth, carry globally significant volumes of organic carbon, and can damage critical seafloor infrastructure. These fast and destructive events are notoriously challenging to measure in action, as they often damage any instruments anchored within the flow. We present the first direct evidence that turbidity currents generate seismic signals which can be remotely sensed (~1-3 km away), revealing the internal structure and remarkably prolonged duration of the longest runout sediment flows on Earth. Passive Ocean Bottom Seismograph (OBS) sensors, located on terraces of the Congo Canyon, offshore West Africa, recorded thirteen turbidity currents over an 8-month period. The occurrence and timing of these turbidity currents was confirmed by nearby moorings with acoustic Doppler current profilers.Results show that turbidity currents travelling over ~1.5 m/s produce a seismic signal concentrated below 10 Hz with a sudden onset and more gentle decay. Comparison of the seismic signals with information on flow velocities from the acoustic Doppler current profilers demonstrates that the seismic signal is generated by the fast-moving front of the flow (frontal cell), which contains higher sediment concentrations compared to the slower-moving body. Long runout flows travelling >1000 km have a fast (3.7-7.6 m s-1) frontal cell, which can be 14 hours, and ~350 km long, with individual flows lasting >3 weeks. Flows travelling >1000 km eroded >1300 Mt of sediment in one year, yet had near-constant front speeds, contrary to past theory. The seismic dataset allows us to propose a fundamental new model for how turbidity currents self-sustain, where sediment fluxes into and from a dense frontal layer are near-balanced.Seismic monitoring of turbidity currents provides a new method to record these hazardous submarine flows, safely, over large areas, continuously for years yet at sub-second temporal resolution. Monitoring these processes from land would considerably ease deployment efforts and costs. Thus, work is underway investigating if terrestrial seismic stations can record submarine seafloor processes in Bute Inlet, a fjord in western Canada where independent measurement of delta-lip failures and turbidity currents can be compared to a passive seismic dataset.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".