Bibliographic record
Abstract
Numerical analysis can provide insight into the effect of ground conditions on seismic signals recorded in downhole seismic testing. As part of a study of the interpretation of seismic cone data in complex ground conditions, this paper deals with the cases of wave propagation in (i) homogeneous soil and (ii) soil of increasing stiffness with depth. The main purpose of this study was to assess the validity of the use of the finite difference program FLAC for the simulation of the downhole seismic test. For realistic assumptions of material stiffness and damping, the main characteristics of field seismic cone penetration test (SCPT) seismic data were reproduced in the simulated data. Both displayed the same general shape of signal, number of oscillations, signal attenuation, frequency content, compression wave component (near-field effect), signal widening, and shift of the peak of the frequency spectrum with depth. Damping was shown to cause signal widening and dispersion, and the shear wave velocity, Vs, interpreted from the simulated wave traces varied with the interval method used to determine it. For a case history of field data, it was found that Vs varied by about 3%, depending on the analysis method used. The results show that finite difference modeling of wave propagation can provide useful insights into the factors affecting the interpretation of downhole seismic tests.Key words: seismic cone testing, shear wave velocity, signal widening, near-field effect, numerical simulation, finite difference.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".