Geologist-controlled trends versus computer-controlled trends: introducing a high-resolution approach to subsurface structural mapping using well-log data, trend surface analysis, and geospatial analysisA companion paper to Mei, S. 2009. New insights on faults in the Peace River Arch region, northwest Alberta, based on existing well-log data and refined trend surface analysis. Canadian Journal of Earth Sciences, <b>46</b>(1): 41–65.
Bibliographic record
Abstract
This paper introduces a refined trend surface analysis (TSA) for detecting faults with small, metre-scale offsets (5–20 m). Conventional TSA uses a global polynomial method to model the trend; the power of the polynomial (e.g., first, second, or third order) is the only parameter for input. The refined approach is different in that it uses local-fit techniques to generate the trend. The refined approach provides greater flexibility for inputting geological knowledge in the trend surface modelling process. This results in a trend surface with the maximum amount of unwanted information, which is removed from the residual surface after removal of the trend from the data, leaving features of interest optimally highlighted in the residuals. Such a trend is referred to as a geologist-controlled trend to differentiate it from the trend surface modelled by conventional TSA, which is primarily a computer-controlled global polynomial surface. The refined approach goes one step beyond conventional TSA by incorporating advanced geostatistics for modelling the trend, interpolating the resultant residuals, and then extracting formation-top offset patterns from the residual surface using spatial analysis. Modelling the geologist-controlled trend in the refined approach results in higher resolution in detecting formation-top offsets and higher accuracy in digitizing fault locations, compared with various techniques that have been traditionally used in subsurface structure mapping of the West Canada Sedimentary Basin (WCSB) (e.g., structural and isopach contour mapping, cross-section construction, and seismic and aeromagnetic data interpretation). The refined approach is demonstrated using the Basal Fish Scale Zone in the Peace River Arch region as an example.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".