JOINT SEISMIC/ELECTRICAL EFFECTIVE MEDIUM MODELLING OF HYDRATE-BEARING MARINE SEDIMENTS AND AN APPLICATION TO THE VANCOUVER ISLAND MARGIN
Bibliographic record
Abstract
Remote determination of the hydrate content of marine sediments remains a challenging problem. In the absence of boreholes, the most commonly used approach involves the measurement of Pwave velocities from seismic experiments. A range of seismic effective medium methods has been developed to interpret these velocities in terms of hydrate content, but uncertainties about the pore-scale distribution of hydrate can lead to large uncertainties in this interpretation. Where borehole geophysical measurements are available, electrical resistivity is widely used as a proxy for hydrate content, and the measurement of resistivity using controlled source electromagnetic methods shows considerable promise. However, resistivity is commonly related to hydrate content using Archie’s law, an empirical relationship with no physical basis that has been shown to fail for hydrate-bearing sediments. We have developed an electrical effective medium method appropriate to hydrate-bearing sediments based on the application of a geometric correction to the Hashin-Shrikman conductive bound, and tested this method by making resistivity measurements on artificial sediments of known porosity. We have adapted our method to deal with anisotropic grains such as clay particles, and combined it with a well-established seismic effective medium method to develop a strategy for estimating the hydrate content of marine sediments based on a combination of seismic and electrical methods. We have applied our approach to borehole geophysical data from Integrated Ocean Drilling Program Expedition 311 on the Vancouver Island margin. Hydrate saturations were determined from resistivity logs by adjusting the geometric factor in areas of the log where hydrate was not present. This value was then used over the entire resistivity log. Hydrate saturations determined using this method match well those determined from direct measurements of the methane content of pressurized cores.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".