Comparison of Elastic Velocity Models for Gas-Hydrate-Bearing Sediments
Bibliographic record
Abstract
One of the distinct physical properties of gas-hydrate-bearing sediments is elevated seismic velocities. A number of velocity models and equations have been presented to describe the effect of gas hydrate on the seismic velocities; e.g., pore-filling model, cementation model, effective medium theory, a weighted equation, and time-average equation. The data set from Mallik 2L-38 gas hydrate research well drilled in northern Canada provided us a unique opportunity to test the velocity models for gas-hydrate-bearing sediments. Velocities predicted from an effective medium theory and those from a weighted equation are compared with observed well log velocities. In the case where there is no gas hydrate in the pore space, P-wave velocities predicted from the effective medium theory are lower than those from the weighted equation when porosity is less than about 30% and higher when porosity is higher than about 30%. For S-waves, effective medium theory predicts generally higher velocities than those from the weighted equation. Both theories predict similar increases in P- and S-wave velocities when gas hydrate occupies the pore space. Even though gas hydrate concentration in the pore space is not known accurately, analyses using both P- and S-wave velocities and their ratios enable us to test the validity of velocity models. Considering only P-wave velocities, there is not much difference between the effective medium and weighted equation. However, considering both P- and S-wave velocities and their ratios, the weighted equation is preferred to the effective medium theory in predicting elastic wave velocities for gas-hydrate-bearing sediments at the Mallik 2L-38 well.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".