Multi-trace 1D Laterally Constrained Impedance Inversion
Bibliographic record
Abstract
Summary We introduce a new approach that uses a lateral constraint to suppress noise and improve the fidelity of formation boundaries in 2D impedance models. A general 1D unconstrained inversion (1D-LUI) framework typically relies on the use of a 1D forward model with each trace being inverted independently following which the 1D impedance estimates are combined together to create a 2D image. This method is fast, however in some cases (e.g. high noise), the resulting 2D impedance model can be noisy making it difficult to distinguish between formation boundaries. To overcome this limitation, while preserving the advantage of low computational cost, a 1D laterally constrained inversion algorithm (1D-LCI) is used to insure that the neighboring 1D models have lateral continuity. Solving the 1D-LCI problem involves the simultaneous inversion of multiple 1D traces producing layered sections with laterally smooth transitions. In addition to enforcing lateral continuity in the inversion model, this algorithm allows for the inclusion of a-priori knowledge from boreholes. We demonstrate the effectiveness of this algorithm on a synthetic 2D model as well as a field seismic dataset.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".