Using constrained inversion of gravity and magnetic field to produce a 3D litho-prediction model
Bibliographic record
Abstract
Geologically constrained inversion of gravity and magnetic field data of the Victoria property (located in Sudbury, Canada) was undertaken in order to update the present three dimensional (3D) geological model. The initial and reference model (used to constrain the inversion) was constructed based on geological information from approximately 965 drillholes. As well, down-hole density and magnetic susceptibility measurements of seven holes were statistically analyzed to derive lower and upper bounds on the physical properties of the lithological units in the reference model. A neural network was trained to predict lithological units from the physical properties measured in seven holes. Then, the trained network was applied on the 3D distribution of physical properties derived from the inversion models to produce a 3D litho-prediction model. Some of the features evident in the lithological model are remnants of the constraints, where the data did not demand a significant change in the model from the initial constraining model. However other changes from the initial model are evident, for example: a larger body was predicted for quartz diorite which may be related to the prospective offset dykes; a new zone was predicted as sulfide which may represent potential mineralization; and a geophysical subcategory of metabasalt was identified with high magnetic susceptibility and high density. The litho-prediction model agrees with the geological expectation for the 3D structure at Victoria, and is consistent with the geophysical data, which results in a more holistic understanding of the subsurface lithology. Presentation Date: Tuesday, October 18, 2016 Start Time: 1:00:00 PM Location: 168 Presentation Type: ORAL
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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".