Inversion Techniques Applied in Stratigraphic Interpretation
Bibliographic record
Abstract
\n The inversion method that we have used is hereafter referred to as the HIGHRES method. It provides estimates of the normal incidence reflection coefficients as a function of lateral and vertical position in the vicinity of a well. More precisely, HIGHRES estimates, trace by trace in the seismic section, amplitudes of the reflection coefficients at each sample in a given time window and at a predefined sampling interval. The well information (well-log-derived reflection coefficients) is necessary for the estimation of the seismic pulse. The algorithm permits a stable estimation of reflection coefficients away from the well with a potential of high resolution (typically 2ms at depths of 2-3000 m). The problem of unstabilities, which frequently occurs with such high resolution estimates, is overcome by the incorporation of the well log data and by the inclusion of coloured noise in the forward model. The validity of the assumptions made in the algorithm depends on the structural and geological complexity of the area to be investigated. Furthermore, the reliability of the estimates will generally decrease with increasing distance from the well location.\n
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".