Synthetic modelling to recognize potential duplex waves from basement faults in western Canada
Bibliographic record
Abstract
Delineation and mapping of basement faults is important in hydrocarbon exploration and induced seismicity projects. In Alberta, basement faults have exerted influence on the depositional setting of sedimentary rocks of the Western Sedimentary Canadian Basin (WSCB). However, the basement roots for these faults may be vertical or near vertical and therefore could be difficult to image. Duplex-wave migration is an imaging tool used to map such vertical features. This synthetic modelling study aims at identifying different scenarios in which the duplex waves could be observable within existing datasets. Finite difference modelling of acoustic waves was used in creating the velocity model and shot records of the wave field of different scenarios. In addition, two field examples are taken from a deep crustal seismic profile acquired as part of the LITHOPROBE PRAISE program. The field geometry has a pattern suitable for the occurrence of the duplex wave energy. The Winagami Reflection Sequence interpreted as sill intrusions are strong sub horizontal reflections observed on the seismic profile. In order to enhance the stability of the process, a base sub horizontal reflection boundary should be specified. These basement features, if properly mapped can add more insights into the nature of fault reactivation and help in developing a tectonic model which could significantly improve predictive capabilities for induced seismicity risk assessment. Presentation Date: Tuesday, September 17, 2019 Session Start Time: 1:50 PM Presentation Start Time: 4:45 PM Location: 304A Presentation Type: Oral
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".