High-resolution geophysical monitoring of moisture accumulation preceding slope movement—a path to improved early warning
Bibliographic record
Abstract
Abstract Slope failures are an ongoing global threat leading to significant numbers of fatalities and infrastructure damage. Landslide impact on communities can be reduced using efficient early warning systems to plan mitigation measures and protect elements at risk. This manuscript presents an innovative geophysical approach to monitoring landslide dynamics, which combines electrical resistivity tomography (ERT) and low-frequency distributed acoustic sensing (DAS), and was deployed on a slope representative of many landslides in clay rich lowland slopes. ERT is used to create detailed, dynamic moisture maps that highlight zones of moisture accumulation leading to slope instability. The link between ERT derived soil moisture and the subsequent initiation of slope deformation is confirmed by low-frequency DAS measurements, which were collocated with the ERT measurements and provide changes in strain at unprecedented spatiotemporal resolution. Auxiliary hydrological and slope displacement data support the geophysical interpretation. By revealing critical zones prone to failure, this combined ERT and DAS monitoring approach sheds new light on landslide mechanisms. This study demonstrates the advantage of including subsurface geophysical monitoring techniques to improve landslide early warning approaches, and highlights the importance of relying on observations from different sources to build effective landslide risk management strategies.
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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 | 0.000 |
| 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) | 0.001 | 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".