Applying laterally varying density corrections to ground gravity and airborne gravity gradiometry data: a case study from the Bathurst Mining Camp
Bibliographic record
Abstract
The influence of topography on gravity and gravity gradiometry measurements is profound and should be minimized prior to geological interpretation. The standard way of minimizing these effects is through the computation of a terrain correction. Terrain corrections require two inputs: topography and density. Often, geology and topography are inextricably intertwined: topography is caused by a change in geology. In geologic environments where there is a structural and (or) stratigraphic control on the near-surface mass distribution, using a single density value in the corrections leads to removal of the topographic effect of rocks having the chosen density. Any remaining gravity signal that correlates with topography is providing geological information. If the objective is to produce a gravity map with minimal topographic signal, then a regionally variable density correction is a means of compensating for this effect. In this paper, we demonstrate how to apply a spatially variable density correction using ground gravity and airborne gravity gradiometry data for the geologically complex Bathurst Mining Camp, northern New Brunswick, Canada. Ground gravity and airborne full tensor gravity gradiometry measurements are subdivided into a series of domains on the basis of the underlying tectonostratigraphic group. Terrain and Bouguer corrections are calculated for each domain using representative density values obtained from drill core and surface sampling throughout the Bathurst Mining Camp. The output from the spatially variable density correction is then compared with previous maps. Overall, the differences are subtle, but the spatially variably density allows for isolated anomalies to be better resolved.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".