Gravity gradiometer data analysis in mineral exploration
Bibliographic record
Abstract
Gravity gradiometer surveys are becoming increasingly important in the search for and characterization of mineral deposits. Measurement of the full gravity gradient tensor provides the opportunity for processing and interpretation of single tensor components or combinations of components. To effectively use these components and combinations thereof, it is necessary to characterize the information content in order to interpret the gradiometer data correctly. We use linear inverse theory to evaluate different components and their combinations and find that which concatenated components produce the smallest modelling errors. Of the single tensor components, the Tzz component was found to provide the best performance overall. Since airborne gradiometer data are collected in a highly dynamic environment, noise is ever-present and must be compensated for to produce a clean signal for interpretation. Two approaches were investigated for removing noise: kriging and directional filtering. The kriging and directional filtering results show a similar level of smoothness, the main difference being the increased smoothing along strike of the directionally filtered data. Since kriging is a data-driven procedure, it provides an objective estimate of the data noise level and degree of smoothness. Based on the kriging results, processing parameters can be chosen to give a similar level of smoothness and noise suppression for directional filtering, that more effectively delineates geological trends in the data.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".