Detection of near-surface hydrocarbon seeps using P- and S-wave reflections
Bibliographic record
Abstract
Abstract The combined use of P- and S-wave seismic reflection data is appealing for providing insights into active petroleum systems because P-waves are sensitive to fluids and S-waves are not. The method presented herein relies on the simultaneous acquisition of P- and S-wave data using a vibratory source operated in the inline horizontal mode. The combined analysis of P- and S-wave reflections is tested on two potential hydrocarbon seeps located in a prospective area of the St. Lawrence Lowlands in Eastern Canada. For both sites, P-wave data indicate local changes in the reflection amplitude and slow velocities, whereas S-wave data present an anomalous amplitude at one site. Differences between P- and S-wave reflection morphology and amplitude and the abrupt decrease in P-velocity are indirect lines of evidence for hydrocarbon migration toward the surface through unconsolidated sediments. Surface-gas analysis made on samples taken at one potential seeping site reveals the occurrence of thermogenic gas that presumably vents from the underlying fractured Utica Shale forming the top of the bedrock. The 3C shear data suggest that fluid migration locally disturbs the elastic properties of the matrix. The comparative analysis of P- and S-wave data along with 3C recordings makes this method not only attractive for the remote detection of shallow hydrocarbons but also for the exploration of how fluid migration impacts unconsolidated geologic media.
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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 | 0.000 | 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".