Time‐lapse impedance inversion using hybrid data transformation and the spike deconvolution method
Bibliographic record
Abstract
Time‐lapse inversion is performed on the difference traces between a monitor and a reference seismic trace. When the underlying difference of the earth's response is sparse and spiky, a spike deconvolution method is preferred for inverting the reflectivity to logarithmic impedance. However, when the structure consists of subresolution gradient ramps or thin layers, sparse spike deconvolution methods can fail to correctly locate the reflectors. A new four‐step method is developed to deal with this situation in the context of time‐lapse monitoring. The processes require some interpretation or expectation of the change in the structure as the first step requires that the difference trace be either differentiated or integrated. Once an appropriate selection is made, a recently developed sparse spike deconvolution algorithm is used to invert the reflectivity which is then converted to impedance. Here the technique is applied to two different synthetic data sets and shows good results even with relatively high 20% Gaussian noise added. We are currently applying this technique to a unique series of high resolution time‐lapse profiles acquired over a steam injection zone.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".