Marine Controlled-Source Electromagnetics and the Assessment of Seafloor Gas Hydrate
Bibliographic record
Abstract
Marine controlled-source electromagnetic (CSEM) methods have become an important and valuable tool in the detection of offshore hydrocarbon targets. The formation resistivity of a sediment layer depends on conductive fluids in interconnected pore spaces. Hydrocarbons increase the formation resistivity of a sediment layer if they form in sufficient quantity to block the pores. CSEM has been used for gas-hydrate evaluation for more than a decade. The common published work contains descriptions of theory, apparatus, data analysis, inverse methods, and interpretation. Here, the fundamentals of time-domain electromagnetics are explained using classical dimensional analysis and are illustrated with a simple approach using data from the northern Cascadia margin, to the west of Vancouver Island, British Columbia, where gas hydrates have been extensively studied. Analyses of CSEM data collected from 1996 to 2005 demonstrated the strong correlation between CSEM resistivities, other geophysical imaging data, and subseafloor hydrocarbons. The analysis is consistent with other intensive studies, including a full gamut of seismic and other geophysical experiments, as well as ground truth from direct sampling and the analyses of cores and logs collected by the Ocean Drilling Program (ODP Leg 146) and its successor, the Integrated Ocean Drilling Program (IODP Expedition 311).
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".