Exploration of continuous seismic recordings with a machine learning approach to document 20 yr of landslide activity in Alaska
Bibliographic record
Abstract
SUMMARY Quantifying landslide activity in remote regions is difficult because of the numerous complications that prevent direct landslide observations. However, building exhaustive landslide catalogues is critical to document and assess the impacts of climate change on landslide activity such as increasing precipitation, glacial retreat and permafrost thawing, which are thought to be strong drivers of the destabilization of large parts of the high-latitude/altitude regions of the Earth. In this study, we take advantage of the capability offered by seismological observations to continuously and remotely record landslide occurrences at regional scales. We developed a new automated machine learning processing chain, based on the Random Forest classifier, able to automatically detect and identify landslide seismic signals in continuous seismic records. We processed two decades of continuous seismological observations acquired by the Alaskan seismic networks. This allowed detection of 5087 potential landslides over a period of 22 yr (1995–2017). We observe an increase in the number of landslides for the period and discuss the possible causes.
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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 | 0.002 | 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) | 0.001 | 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 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".