Seismic and Acoustic Monitoring of Submarine Landslides
Bibliographic record
Abstract
Submarine landslides pose a hazard to coastal communities and critical seafloor infrastructure, occurring on all of the world's continental margins, from coastal zones to hadal trenches. Offshore monitoring has been limited by the largely unpredictable occurrence of submarine landslides and the need to cover large regions. Recent subsea monitoring has provided new insights into the preconditioning and run-out of submarine landslides using active geophysical techniques. However, these tools measure a small spatial footprint and are power- and memory-intensive, thus limiting long-duration monitoring. Most landslide events remain unrecorded. In this chapter, we first show how passive acoustic and seismologic techniques can record acoustic emissions and ground motions created by terrestrial landslides. This terrestrial-focused research has catalyzed advances in characterizing submarine landslides using onshore and offshore networks of broadband seismometers, hydrophones, and geophones. We discuss new insights into submarine landslide preconditioning, timing, location, velocity, and down-slope evolution arising from these advances. Finally, we outline challenges, emphasizing the need to calibrate seismic and acoustic signals generated by submarine landslides. Passive seismic and acoustic sensing has a strong potential to enable more complete hazard catalogs to be built and open the door to emerging techniques (such as fiber-optic sensing) to fill key knowledge gaps.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".