AN APPLICATION OF PRINCIPAL COMPONENTS ANALYSIS TO BOREHOLE GEOPHYSICAL DATA
Bibliographic record
Abstract
When multiple geophysical tests are performed in a well the resulting suite of logs form a set of multivariate data. It is commonly known that many of the geophysical tests respond to similar, and often inter-related geologic factors. While cross-plotting can be used to study relationships between pairs of logs, it fails to clearly present the relationships within the data. Principal Components Analysis (PCA), however, can provide additional insight by transforming the log data into a new orthogonal, yet simplified, coordinate system. The process of PCA involves three main steps: log correlation analysis, eigenvector interpretation, and component score cross-plotting. From this process the log analyst can gain an understanding of the factors controlling the variation within the data, and determine the uniqueness of the electrofacies encountered in the borehole. The PCA technique was applied to suites of logs from the Kitchener-Waterloo region of Southern Ontario. Previous analysis of the data included cross plotting of the various logs to determine the uniqueness of the lithofacies. The first stage of the PCA, correlation analysis, indicated the dominance of porosity and clay content variation. The correlation between density and porosity, however, varied significantly between wells. Eigenvector interpretation indicated that 78 percent of the total data variation was described by the first two principal components (63 and 15 percent, respectively), confirming the high degree of inter-relationship between the log measurements. Cross-plots of the data with respect to the components indicates the uniqueness of the various till units encountered in the region.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".