Multitrace impedance inversion with lateral constraints
Bibliographic record
Abstract
ABSTRACT We have developed a lateral constraint to the inversion of 1D seismic impedance models to suppress the effect of data noise and improve the fidelity of formation boundaries in 2D models for situations with dips of less than 20°. Typical inversion frameworks rely on a 1D forward model with each 1D trace being inverted independently. Adjacent inversion models are combined together to form a 2D impedance model. Adding a lateral constraint improves the fidelity of the 2D impedance models while retaining much of the advantage of the low-computational cost associated with typical 1D inversion schemes. Solving the 1D lateral constraint inversion (1D-LCI) problem involves the simultaneous inversion of multiple 1D traces producing layered sections with lateral smoothed transition. In addition to enforcing lateral continuity in the inversion model, this algorithm allows for the inclusion of a priori knowledge from boreholes. We determined the effectiveness of this algorithm on two synthetic models, as well as a field seismic data set. One-dimensional-LCI inversion results produced well-defined horizontal boundaries, while suppressing noise.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".