Stochastic inversion of a gravity field on multiple scale parameters using surface and borehole data
Bibliographic record
Abstract
ABSTRACT A 3D stochastic inversion method based on a geostatistical approach is presented for three‐dimensional inversion of gravity on multiple scale parameters using borehole density and gravity and surface gravity. The algorithm has the capability of inverting data on multiple supports. The method involves four main steps: i) upscaling of borehole densities to block densities, ii) selection of block densities to use as constraints, iii) inversion of gravity data with selected block densities as constraints and iv) downscaling of inverted densities to small prisms. Two modes of application are presented: estimation and simulation. The method is first applied to a synthetic stochastic model. The results show the ability of the method to invert surface and borehole data simultaneously on multiple scale parameters. The results show the usefulness of borehole data to improve depth resolution. Finally, a case study using gravity measurements at the Perseverance mine (Quebec, Canada) is presented. The recovered 3D density model identifies well three known deposits and it provides beneficial information to analyse the geology of massive sulfide for the domain under study.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".