Tomographic imaging of permafrost using three-component seismic cone-penetration test
Bibliographic record
Abstract
Abstract We conducted seismic cone-penetration tests (SCPT) and tomographic imaging in a permafrost mound in northern Quebec, Canada, to study the cryostratigraphy and assess the seismic properties of permafrost at temperatures near 0°C. A swept impact source generating both P- and S-waves and penetrometer-mounted three-component accelerometers were used to acquire surface-to-depth first-arrival times as input to produce 2D images of P- and S-wave velocities. Based on the three-component accelerometer records and the propagation modes of body waves, the P- and S-wave first arrivals were detected and discriminated. The inversion of the first-arrival times was based on the simultaneous iterative reconstruction technique. The multioffset surface-to-depth geometry used in this study limits the lateral resolution of tomographic imaging. However, the vertical variation in seismic velocities in the permafrost mound shows good reproducibility and can be compared to the cone data. The gathering of cone data such as cone resistance, friction ratio, electrical resistivity, and temperature, along with the seismic velocities, provides new insights into the cryostratigraphy of permafrost. While the cone data are affected by the vertical heterogeneity because of the complex sequence of ice lenses and frozen soil layers of a few centimeters thickness, the smooth velocity variations of P- and S-waves characterized by a wavelength of a few meters depend on the bulk physical properties of permafrost. The P- and S-wave velocities varied from 2400to3200m/s and from 850to1750m/s, respectively, for a temperature range between 0°C and −2°C. At this temperature range, the variations in unfrozen water content are important and affect directly the seismic properties of permafrost. The decrease in P- and S-waves velocities in depth with the permafrost mound depends nonlinearly on the increase of unfrozen water content from 9% to 30% for a temperature increase from −2°Cto0°C.
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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) | 0.000 | 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 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".