A practical method for estimating effective parameters of anisotropy from reflection seismic data
Bibliographic record
Abstract
Abstract The location of any event imaged by P-wave reflection seismic data beneath a tilted transversely isotropic (TTI) overburden is shifted laterally if isotropic velocities are used during data processing. The magnitude of the shift depends on five independent parameters: overburden thickness, angle of tilt, symmetry-axis velocity, and the Thomsen anisotropy parameters ε and δ. The shift also varies with source–receiver offset. We have developed a procedure to estimate these five parameters when the tilt of the symmetry axis from the vertical is equal to the dip of the TTI layer (except in the special cases transverse isotropy with vertical or horizontal axis of symmetry). We observe three attributes of seismic data processed using isotropic velocities: the zero-offset arrival time of a selected reflection, the difference in arrival time between a near-offset and a far-offset arrival, and the difference in imaged location (smear) of this target event between the same offsets. We then perform a cascaded scan of the five parameters to determine those combinations of the five that result in calculated attributes equivalent to the observed attributes. The multiple solutions are averaged to give the parameter estimates. Application of this method to synthetic and physical model reflection data results in multiple solutions, which are constrained and averaged to obtain the effective imaging parameters. These effective parameters are close estimates of the true model parameters in both cases. For field seismic data this procedure requires that there be a suitable observable event below the TTI overburden and assumes that the measured times and shifts are reasonably accurate.
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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".