Impedance joint inversion of borehole and surface seismic data
Bibliographic record
Abstract
The impedance inversion for single surface seismic data is limited by the bandwidth of the seismic data and subject to a large degree of non-uniqueness. Joint inversion with other high frequency geophysical data has great potential to improve the resolution and reduce the ambiguity of the inversion result. The borehole seismic data have the advantages of less attenuation, higher resolution, wider frequency bandwidth and being closer to the reservoir target than the surface seismic data, which is valuable to improve the surface seismic inversion. We built an impedance joint inversion workflow based on the Bayes theorem. The borehole seismic and surface seismic data are integrated together with the likelihood function and the sparse priori distribution of the reflectivity is designed in accordance with the field logging data characteristic. Two practical cases of the impedance joint inversion with the borehole seismic data and the surface seismic data were presented. It is obvious that the impedance joint inversion method provides a substantial improvement with respect to the constrained sparse spike inversion result. Consequently, this joint inversion is a promising technology for reservoir characterization.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".