The Improvement of 3D Traveltime Tomographic Inversion Method
Bibliographic record
Abstract
As 3D high-precision seismic exploration is more and more widely used in seismic data acquisition, traveltime tomographic inversion based on first arrivals is developed from 2D to 3D. However, magnanimity data of 3D traveltime inversion brings about the problem of data storage; the absence of first arrivals with near offset reduces the precision of shallow layer; the utilization of prior information, such as small refraction and micro-logging data, can improve the precision of 3D traveltime inversion. Therefore, we make some improvements in 3D traveltime inversion method. We take compression storage for large and sparse matrix, propose virtual receivers technology, and add prior information to tomographic inversion linear equations. The application in 3D real data indicates that the improvements can effectively improve 3D traveltime tomographic inversion. Key words : 3D seismic exploration; 3D traveltime inversion method; 3D traveltime tomographic inversion
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".