AVO inversion of BSRs in marine gas hydrate studies
Bibliographic record
Abstract
Abstract We examine the usefulness of amplitude versus offset (AVO) analysis of bottom-simulating reflections (BSRs) for estimating associated marine gas hydrate and free-gas concentrations. A nonlinear Bayesian inversion is applied to estimate marginal probability distributions (MPDs) of physical parameters at a BSR interface, which are related to overlying gas hydrate and underlying free-gas concentrations via rock physics modeling. The problem is constrained further by prior information and re-parameterization of inversion results. Inversion of BSR AVO data from offshore Vancouver Island, Canada, shows that gas hydrate and free-gas concentrations are, respectively, 0%–23% and0%–2% of the pore volume, at a 90% credibility level. This result indicates that the data do not provide sufficient information to independently resolve gas hydrate and free-gas concentrations to useful accuracy. The study is directed primarily at AVO for gas-hydrate-related BSRs, but may have important applicability in testing the degree of constraint of formation characteristics in other AVO studies. The inversion method is applied also to synthetic AVO data generated from Ostrander's gas-sand model. In this case, MPDs sufficiently constrain the relationship between P- and S-wave velocities in the sandstone unit to determine if it is gas-charged. The variable degree of model constraint obtained in this AVO study highlights the need to include rigorous quantitative uncertainty analysis in all AVO studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".