Seismic attributes in unconsolidated near-surface sediments
Bibliographic record
Abstract
The calculation, analysis and interpretation of seismic attributes is an important tool in reservoir seismology and has been used since the 1950’s to improve the characterization of oil and gas fields around the world. Recent applications of seismic attributes in the near surface use the coherency attribute to detect faults as well as average and normalized frequencies and similarity to map ground instability and image sinkholes. Seismic attributes are also used to assess unconsolidated oil sand reservoirs, to evaluate ocean/lake bottom responses from unconsolidated sediments, and to visualize the internal structure of mass transport deposits. In unconsolidated near-surface sediments seismic attributes are rarely used. This is likely due to the high variability present in the near surface. Combined P-/S-attributes are difficult to obtain because of the large difference between P-wave and S-wave velocity, as well as frequency and resolution. Therefore, the most important step to obtain these combined attributes is performing depth conversions for the P- and S-wave reflection profiles that perfectly match horizons and features in the depth domain. We present commonly used attributes calculated from a shear-wave reflection profile imaging the dome structure of an esker. Attributes calculated from the compressional-wave reflections are compared to the shear-wave attributes which benefit from higher resolution than P-wave attributes. We highlight the attributes which best enhance the general subsurface structure and list new information gained from different attributes.
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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 | 0.002 | 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) | 0.004 | 0.001 |
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".