A case study showing the value of multioffset synthetic seismograms in seismic data interpretation
Bibliographic record
Abstract
Abstract A baseline 3D3C seismic survey was acquired in May 2014 at a Field Research Station in Southern Alberta, Canada, which is the site of experimental CO2 injection into an Upper Cretaceous sandstone at approximately 300 m depth. We have created synthetic seismograms from sonic and density logs to identify reflectors seen on the processed seismic data. The high-amplitude positive response (peak) at the top of the Upper Cretaceous Milk River Formation sandstone on the normal incidence PP synthetic seismogram does not match the response seen on the migrated PP seismic data, which is a very low amplitude peak. For such a high impedance, low Poisson’s ratio sandstone, the Zoeppritz equations predict a high-amplitude reflection coefficient at zero offset, then a decrease in amplitude, and even a change in polarity with increasing source-receiver offset. To match the stacked seismic data better, we have created offset synthetic seismograms using P- and S-wave sonic logs and density logs. The character of the top Milk River reflection on the seismic data stacked using all offset traces resembles that observed on the stacked offset synthetic seismogram, which is a similar low-amplitude peak. The character of the top Milk River reflection on the seismic data stacked using only near-offset traces to 250 m looks like that seen on the normal incidence synthetic seismogram.
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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 | 0.001 | 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".