Bibliographic record
Abstract
Our ultimate goal of seismic data processing is to produce seismic images with correct lateral and vertical positions, and with amplitude proportional to the reflection coeffi- cients in the subsurface. A good migrated image depends strongly on the accuracy of the velocity model. Prestack depth migration (PSDM) is a powerful tool not only for imaging but also for velocity model building. The main techniques used for migration velocity analysis include vertical updating and tomography analysis , including anisotropy parameters estimation. To achieve the goal of accurately positioning the seismic events in subsurface, the velocity anisotropy should be accounted for. If the presence of velocity anisotropy is important, then ignoring it will significantly degrade the final image accuracy. This thesis examines a frontier exploration survey acquired off the East Coast of Canada by Statoil Canada Ltd. and processed by CGG. Kirchhoff isotropic PSDM and tilted transverse isotropy (TTI) PSDM were conducted with the goals of good imaging and good velocity estimation. Structural interpretation and Amplitude Versus Offset (AVO) analysis for this survey demonstrate the benefits of TTI PSDM, whose subsurface images significantly reduced the exploration risk compared with isotropic PSDM. Estimation of reliable anisotropy parameters is challenging, especially for the survey studied in this thesis where no well information is available. This thesis presents a practical production method for building five parameter fields needed for TTI PSDM: p- wave velocity, Thomsen’s anisotropy parameters and the angles describing the symmetry axis of the anisotropy. This method includes 1D joint inversion (Huang, 2007) to estimate anisotropy parameter and high resolution tomography (Hu, 2011) to obtain accurate velocity models.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".