Introduction to this special section: Imaging/inversion: Estimating the earth model
Bibliographic record
Abstract
Most of our efforts as oil and gas geoscientists are directed toward estimating the earth model. In addition, most of our earth-model estimation involves inversion of one kind or another, even if we don't think explicitly in terms of inversion. When we specialize to seismic, as all seven articles in this special section do, “Imaging/inversion: Estimating the earth model” describes our work. Seismic is a big field, however, and we have not always regarded it as being so unified. After all, what could topics as different as (for example) seismic imaging of structural targets and estimation of reservoir properties possibly have in common? As it turns out, the answer is “plenty,” and our realization of this fact has helped us get better at our jobs; our structural images benefit from knowledge of rock and reservoir properties, and our rock-property estimates improve when we use information from seismic imaging. So the historic concept of a “seismic chain,” whose separate links are acquisition, preprocessing, imaging, inversion, and interpretation, is gradually breaking down as we realize how each step quantitatively influences all the others. In fact, as some of the articles in this section illustrate, the sequential chain is being replaced by a more sophisticated set of feedback loops where the derived information may be used to improve the final result.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.021 | 0.021 |
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".