Robust quantitative estimation of the seismic attenuation from shallow geotechnical borehole VSP data
Bibliographic record
Abstract
ABSTRACT Estimating seismic attenuation from shallow geotechnical borehole surveys can be a delicate task. Measured data are often collected within the source near-field domain and the classic inverse-distance correction of the geometric spreading (GS) is inappropriate at this scale. We develop a novel approach based on a 3D full-waveform modeling to substitute the inverse-distance correction. It consists of scaling the picked amplitudes using their counterparts obtained from an elastic simulation carried out under conditions mimicking the data acquisition. The seismic attenuation may be inferred from the corrected amplitudes using either a piecewise regression or a ray-based inversion. Numerical experiments involving P- and S-wave synthetic data indicate that our correction better compensates for the GS effect than the inverse-distance correction. For a synthetic example with 5% noise, the Q-factor values derived from amplitude corrected via the proposed approach have a relative error of approximately 10% compared with 40% for the traditional correction. We investigate the effect of the velocity and density uncertainty upon the calculated correction terms and show that our approach is unbiased and stable. Finally, the robustness of our workflow is assessed on a real case study involving a P-wave data set acquired in a geotechnical borehole.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".