A robust workflow for performing joint impedance inversion and its applications
Bibliographic record
Abstract
Abstract Multicomponent seismic data offer several advantages for characterizing reservoirs with the use of the vertical component (PP) and mode-converted (PS) data. Joint impedance inversion inverts both of these data sets simultaneously; hence, it is considered superior to simultaneous impedance inversion. However, the success of joint impedance inversion depends on how accurately the PS data are mapped on the PP time domain. Normally, this is attempted by performing well-to-seismic ties for PP and PS data sets and matching different horizons picked on PP and PS data. Although it seems to be a straightforward approach, there are a few issues associated with it. One of them is the lower resolution of the PS data compared with the PP data that presents difficulties in the correlation of the equivalent reflection events on both the data sets. Even after a few consistent horizons get tracked, the horizon matching process introduces some artifacts on the PS data when mapped into PP time. We have evaluated such challenges using a data set from the Western Canadian Sedimentary Basin and then develop a novel workflow for addressing them. The importance of our workflow was determined by comparing data examples generated with and without its adoption.
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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 | 0.000 |
| 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".