Fault imaging in hydrothermal dolomite reservoirs: A case study
Bibliographic record
Abstract
Abstract Most hydrocarbon fields found within the Ordovician Trenton and Black River Groups of the eastern United States and eastern Canada are associated with basement-related faults. These faults are best imaged by 3D seismic technology. Because of environmental conditions, seismic data often are contaminated by noise that masks fault terminations and reduces signal-to-noise ratio. As a result, seismic horizons are discontinuous, calculated coherence values are affected adversely, and horizon interpretation and fault identification are difficult, if not impossible. Poststack processing is required to attenuate this noise before an optimal interpretation can be done. We conducted a three-step poststack processing flow to attenuate noise and highlight fault terminations. Noise-reducing algorithms consist of frequency-space (f-x) deconvolution, zero-phase filtering, and τ-p filtering. The structural grain of major faults identified with these techniques agrees with the regional strike of major faults previously defined in the area. These faults were confirmed by drilling results. Our calculated semblance of cosine of phase provides better fault definition than does regular coherency and serves as an alternative attribute for mapping faults. The processing sequence could apply in areas with similar geologic settings and noise problems.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".