A new constrained velocity tomography algorithm using geostatistical simulation
Bibliographic record
Abstract
A new constrained velocity tomography algorithm is presented. This algorithm is based on slowness covariance modeling using experimental travel time covariance. Slowness and travel time covariances allow cokriging and simulation of slowness fields, between two boreholes, fitting the measured travel times. Cells with known velocities, for example the cells crossed by the holes, provide velocity constraints which are easily implemented. The proposed approach is compared to the classical LSQR algorithm using a synthetic model and real data collected for geotechnical evaluation in a karstic area. In each case, constrained and non-constrained LSQR, cokriging and simulation were performed. The tomographies on synthetic model show that geostatistical methods provide comparable to or better results than LSQR. For both methods, additional velocity constraints reduce uncertainty and improve spatial resolution of the inverted velocity field. Also, the simulation on synthetic model increases the spatial resolution compared to LSQR. It is demonstrated that the method is robust with regard to an acceptable level of random noise on velocity constraints. The real data analysis shows that the proposed method gives very consistent results in regard to the drilling log information.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".