A robust workflow for performing joint impedance inversion with applications from North American Basins
Bibliographic record
Abstract
Multicomponent seismic data offers many advantages for characterizing reservoirs with the use of the vertical component (PP) and the mode-converted (PS) data. Joint impedance inversion inverts both these datasets simultaneously, and hence is considered superior to simultaneous impedance inversion. However, the success of joint impedance inversion depends on how accurately the PS data is mapped on the PP time domain. Normally, this is attempted by following well-to-seismic ties for both PP and PS datasets, and the matching of different horizons picked on both PP and PS data. Though, 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 than the PP data which presents difficulties in the correlation of the equivalent reflection events on both the datasets. Even if few consistent horizons get tracked, the horizon matching process introduces some artifacts on the PS data mapped into PP time. In this exercise, we elaborate on such challenges with a dataset from the Western Canadian Sedimentary Basin and then propose a novel workflow for addressing them. The value addition provided by the proposed workflow has been demonstrated by comparing data examples generated both with and without its adoption Presentation Date: Tuesday, September 17, 2019 Session Start Time: 9:20 AM Presentation Time: 9:20 AM Location: Poster Station 1 Presentation Type: Poster
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".