Modeling earthquake-induced seiche process and subsequent homogenite deposits in lacustrine setting
Bibliographic record
Abstract
Earthquakes imprint their signatures on lake sediments through event deposits, which serve as paleoseismological archives. Among these deposits, homogenite layers are commonly associated with lake oscillations, i.e., seiches. Here, we investigate the seiche mechanism and the formation of homogenite related sediment deposit within a lacustrine environment. This study focuses on the 1822 CE earthquake in the Western European Alps, which triggered subaqueous landslides in Lake Bourget (France). This event caused oscillations in the lake's water, which subsequently resulted in the formation of a homogenite layer in the deep basin. The underlying mechanism is resolved by presenting the first comprehensive numerical model via coupling coseismic displacement, seismic wave propagation, and mass movement with the tsunami model. The numerical simulations show excellent agreement with the geological and historical observations. The water disturbances caused by subaqueous landslides generated small tsunami waves with a maximum runup height of approximately 2.5 m. By analyzing the tsunami signals via Fourier spectral analysis and fast iterative filtering, we determined that seismic waves are the primary drivers of seiche, which excite the natural modes of Lake Bourget. Our findings confirm that the sediments found in the deep basin originated from one main subaqueous landslide and from tsunami erosion of littoral sands (backwash). However, the seismically induced seiche was solely responsible for keeping the fine-grained sediment cloud in suspension for several days and led to the formation of the homogenite layer (or seiche deposit) with typical grain orientation characteristics and could also be effective in identifying whether landslides or delta collapses (linked to homogenite/megaturbidites) in closed lakes were triggered by seismic or nonseismic sources which is crucial for reconstructing the history of past earthquakes and associated hazards.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".