Muon Geotomography—Bringing New Physics to Orebody Imaging
Bibliographic record
Abstract
Abstract Muon geotomography, a novel geophysical exploration and imaging technology, uses cosmic rays to create three-dimensional (3-D) images of subsurface density distributions. The first controlled field test confirming the capability of muon geotomography for imaging a dense orebody in a complex geologic environment was conducted at the Price volcanic-hosted massive sulfide (VHMS) deposit, Vancouver Island, British Columbia, Canada. The semimassive and massive polymetallic mineralization of the Price deposit is situated in a Paleozoic stratigraphic package of rocks known as the Sicker Group including the Price, Myra, Thelwood, and Flower Ridge Formations, indicative of volcanic rocks formed in a rifted oceanic island-arc system. The field application involved placing a sensor with an active area of 1 m 2beneath the massive sulfide orebody in an underground tunnel for exposures of about two weeks at several locations. Muon flux data were inverted to recover a 3-D density image of the deposit. The inverted data were in good agreement with drill core data. However, some distortions of the image were observed due to the limitations imposed by the available tunnel which restricted the angular views available to the sensors. Muon geotomography works best when sensors are placed such that they can view the region under study from a range of different angles. The demonstrated ability to perform accurate forward model simulations makes the sensitivity of the technique predictable for specific survey situations. The results demonstrate the potential of muon geotomography for identification and characterization of orebodies located in complex geologic environments. Three-dimensional images from muon geotomography surveys may be used to guide drilling operations toward regions of high-density contrast, thereby significantly reducing costs and environmental impact associated with locating orebodies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".