On the use of computerized tomography scan analysis to determine the genesis of very high resolution seismic reflection facies
Bibliographic record
Abstract
Computerized tomography scanner (CT scanner) is a powerful tool that can reveal millimeter‐scale stratigraphy from cores. The coupling of very high resolution (VHR) seismic data with CT scan data represents a new approach to determine with more confidence the genesis of seismic facies. However, prior to the application of such a method, a clear understanding of the relationship between these two types of data (e.g., physical parameters in common) is necessary. The application of CT scan analysis along with VHR seismic data showed that seismic and CT scan signals respond to different physical properties of the sediments. Results suggest that similar seismic facies may correspond to different CT scan facies and, conversely, that different CT scan facies may have the same acoustic response. Sedimentary structures observed on the CT scan imagery suggest that a seismic response should be linked to a synthesis of lithological variations instead of a single and unique lithology change/contrast. Furthermore, this study points toward the fact that the geometry of the reflections does not always correspond to the geometry of sedimentary structures but rather to the anisotropy of the physical properties of the deposit. Consequently, to give a genetic connotation to the internal reflection pattern of a seismic facies can be misleading in some cases. Finally, the fine‐scale sedimentological analysis based on very high resolution CT scan data is useful to determine very fine seismic analogs and thus to better explain the seismic expression of a deposit.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".