Near-surface VSP surveys using the seismic cone penetrometer
Bibliographic record
Abstract
Abstract We have found that high-quality vertical seismic profile (VSP) data can be collected for near-surface applications using the seismic cone penetrometer. Cone-mounted accelerometers are used as the VSP receivers, and a sledgehammer against the cone truck base plate is used as a source. This technique eliminates the need to drill a borehole, thereby reducing the cost of the survey, and results in a less invasive means of obtaining VSP data. Two SH-wave VSP surveys were acquired over a deltaic sand/silt sequence and compared to an SH-wave common-depth-point (CDP) reflection profile. The VSP data were processed using a combination of singular-value-decomposition filtering, deconvolution, and f-k filtering to produce the final VSP extracted traces. The VSP traces correlate well with cone geotechnical logs and the CDP surface-seismic data. The first breaks from the VSP can be used to generate shear-wave velocity profiles that are important for time-to-depth conversion and the velocity correction of the CDP surface data.
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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 | 0.001 |
| 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".