Improved edge detection mapping through stacking and integration: a case study in the Bathurst Mining Camp
Bibliographic record
Abstract
ABSTRACT Airborne geophysical surveys provide spatially continuous regional data coverage, which directly reflects subsurface petrophysical differences and thus the underlying geology. A modern geologic mapping exercise requires the fusion of this information to complement what is typically limited regional outcrop. Often, interpretation of the geophysical data in a geological context is done qualitatively using total field and derivative maps. With a qualitative approach, the resulting map product may reflect the interpreter's bias. Source edge detection provides a quantitative means to map lateral physical property changes in potential and non‐potential field data. There are a number of Source edge detection algorithms, all of which apply a transformation to convert local signal inflections associated with source edges into local maxima. As a consequence of differences in their computation, the various algorithms generate slightly different results for any given source depth, geometry, contrast, and noise levels. To enhance the viability of any detected edge, it is recommended that one combines the output of several Source edge detection algorithms. Here we introduce a simple data compilation method, deemed edge stacking, which improves the interpretable product of Source edge detection through direct gridding, grid addition, and amplitude thresholding. In two examples, i.e., a synthetic example and a real‐world example from the Bathurst Mining Camp, New Brunswick, Canada, a number of transformation algorithms are applied to gridded geophysical data sets and the resulting Source edge detection solutions combined. Edge stacking combines the benefits and nuances of each Source edge detection algorithm; coincident or overlapping and laterally continuous solutions are considered more indicative of a true edge, whereas isolated points are taken as being indicative of random noise or false solutions. When additional data types are available, as in our example, they may also be integrated to create a more complete geologic model. The effectiveness of this method is limited only by the resolution of each survey data set and the necessity of lateral physical property contrasts. The end product aims at creating a petrophysical contact map, which, when integrated with known lithological outcrop information, can be led to an improved geological map.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".