Geotechnical site characterization using multichannel analysis of surface waves: A case study of an area prone to quick‐clay landslides in southwest Sweden
Bibliographic record
Abstract
ABSTRACT Quick‐clay landslides are important geohazards in Sweden, Norway and Canada. While they have been studied using various geotechnical and geophysical methods, only a handful of seismic surveys have been reported for their studies. Here, we reprocess active‐source seismic data from a quick‐clay landslide site in southwest Sweden to complement earlier studies of reflection imaging and first‐break traveltime tomography with surface‐wave dispersion analysis. Results suggest extremely low shear‐wave velocities, even as low as 60–100 m/s. From a geotechnical perspective, this implies that the region classifies as a high‐risk zone for landslides and construction purposes. High or anomalous values of Poisson's ratio (or similarly P‐ and S‐wave velocity ratio) depict a zone within the normally consolidated sediments that likely represents a coarse‐grained layer, thus confirming earlier results from a number of boreholes drilled in the study area. Overall, the results presented further support to the previous hypothesis that the coarse‐grained layer plays a major role in the formation and creation of quick‐clay landslides in the study area. Additionally, an attempt to model the distribution of potential quick clays along one of the seismic profiles is performed through a combination of the modelled geophysical properties and soil textures. This study illustrates the potential of seismic methods, and how the integration of multiple geophysical properties and different data handling strategies can help to accurately characterize regions susceptible to quick‐clay landslides.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".