Improve impedance inversion by adopting seismic sedimentary-guided a priori model
Bibliographic record
Abstract
Abstract We have developed an innovative procedure for model-based seismic inversion in areas with sparse or clustered and biased well control, where lithofacies and geobodies cannot be adequately sampled in wells and correctly represented in a priori acoustic impedance (AI) models constructed with conventional kriging methods. We have applied seismic sedimentology for facies mapping before the model building process. Lithology-calibrated seismic stratal slices contain rich information for analyzing sedimentary geomorphology and dispersal patterns of depositional systems, providing independent geologic knowledge to constrain a priori models. The new information can be incorporated by directional kriging that properly addresses facies types, orientations, and facies boundary conditions. Finally, the improved a priori model can be applied in Bayesian inversion for an updated inverted AI volume. This procedure was applied in a 3D project in Saidong Depression, Erlian Basin, China, with promising results, achieving inverted AI maps with a more complete facies representation, a more reasonable sediment dispersal pattern (orientation), and clearer facies boundaries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".