Integration and Use of Hybrid Deterministic Inversion in Quantitative Sequence Stratigraphy Workflow
Bibliographic record
Abstract
Summary Seismic and well based sequence stratigraphy is the interpretation of interfaces between significant surfaces. It is often not integrated with the inversion products due to lower temporal resolution of the inversion results. In our workflow we have created a Hybrid Deterministic Inversion where deterministic pre-stack simultaneous inversion derived from the bandlimited seismic data is converted to rock properties. The neural network inversion in our case is limited to enhance the results so the sequence stratigraphic framework is better understood. In the stage of rock property prediction, the pre-stack inversion volumes can also be combined with post-stack acoustic impedance inversion. The resulting quantitative sequence stratigraphic geological model through this hybrid inversion thus have interpretive and quantitative attributes to reduce the exploration and development risk. The workflow is applied to a complex incised valley fill sequence in Southern Alberta distinguishing lithology and porosity of prolific channel systems from the lithic floodplain silts and shales. Importance of pre-stack data conditioning and geological input to the model is emphasized and an integrated workflow is defined.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".