Joint inversion of seismic traveltimes and gravity data on unstructured grids with application to mineral exploration
Bibliographic record
Abstract
Seismic methods continue to receive interest for use in mineral exploration due to the much higher resolution potential of seismic data compared to the techniques traditionally used, namely gravity, magnetics, resistivity and electromagnetics. However, the complicated geology often encountered in hard‐rock exploration can make data processing and interpretation difficult. Inverting seismic data jointly with a complimentary dataset can help overcome these difficulties and facilitate the construction of a common Earth model. We consider the joint inversion of seismic traveltimes and gravity data. Our joint inversion approach incorporates measures of model similarity (i.e. slowness versus density) that are both compositional and structural in nature and follow naturally from this specific data combination. We perform the inversions on unstructured grids comprised of triangular cells in 2D, or tetrahedral cells in 3D. We present our joint inversion method on a scenario inspired by the Voisey's Bay massive sulphide deposit in Labrador, Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".