Seismic and Acoustic Monitoring of Submarine Landslides: Ongoing Challenges, Recent Successes and Future Opportunities
Bibliographic record
Abstract
Submarine landslides pose a hazard to coastal communities due to the tsunamis they can generate, and can damage critical seafloor infrastructure, such as the network of cables that underpin global data transfer and communications. These mass movements can be orders of magnitude larger than their onshore equivalents and are found on all of the world’s continental margins; from coastal zones to hadal trenches. Despite their prevalence, and importance to society, offshore monitoring studies have been limited by the largely unpredictable occurrence of submarine landslide and the need to cover large regions of extensive continental margins. Recent subsea monitoring has provided new insights into the preconditioning and run-out of submarine landslides using active geophysical techniques, but these tools only measure a very small spatial footprint, and are power and memory intensive, thus limiting long duration monitoring campaigns. Most landslide events therefore remain entirely unrecorded. Here we first show how passive acoustic and seismologic techniques can record acoustic emissions and ground motions created by terrestrial landslides. We then show how this terrestrial-focused research has catalysed advances in the detection and characterisation of submarine landslides, using both onshore and offshore networks of broadband seismometers, hydrophones and geophones. We then discuss some of the new insights into submarine landslide preconditioning, timing, location, velocity and their down-slope evolution that is arising from these advances. We finally outline some of the outstanding challenges, in particular emphasising the need for calibration of seismic and acoustic signals generated by submarine landslides and their run-out. Once confidence can be enhanced in submarine landslide signal detection and interpretation, passive seismic and acoustic sensing has strong potential to enable more complete hazard catalogues to be built, and opens the door to emerging techniques (such as fibre-optic sensing), to fill key, but outstanding, knowledge gaps concerning these important underwater phenomena.
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 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) | 0.001 | 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) | 0.001 | 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 teacher head, 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".