GPR, TDR, and geochemistry measurements above an active gas vent to study near-surface gas-migration pathways
Bibliographic record
Abstract
Abstract The migration of deep gas to the atmosphere along faults and associated structures is important in many fields, from studying the natural contribution of atmospheric greenhouse gases leaking from geothermal areas to ensuring the safety of man-made natural gas and carbon dioxide (CO2) geologic-storage sites. Near-surface geophysical and geochemical techniques were applied to a naturally occurring gas vent located along a deep terrestrial fault to better understand the structure and geophysical response of this gas-migration pathway. A number of ground-penetrating radar (GPR) profiles were first conducted across the vent. Spot samples were then measured along one of these profiles for in situ apparent permittivity (using time-domain reflectometry — TDR), complex permittivity on dried samples (using a capacitivecell), soil-gas composition, and clay and bulk mineralogy. Results show how the migrating gas induces secondary effects that modify the signature of the vent as seen in the GPR profiles. In particular, high flux rates across the vent core (i.e., the central portion of the vent) result in a total lack of vegetation, which in turn leads to high water content that is likely responsible, together with increased bulk conductivity, for GPR signal blanking. In the transition zone surrounding the core, a water-content minimum and other changes contribute to a deeper GPR signal penetration that highlights dipping events. Data show that the structure itself is slightly asymmetrical, which may indicate more fracturing to one side of the feature. This study shows that GPR surveys are capable of imaging secondary effects induced by gas migration in soils above a structural discontinuity, even if the structure itself cannot be imaged.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".