Seismic and Acoustic Monitoring of Submarine Landslides
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it