Deep mineral exploration using multi-scale electromagnetic geophysics: the Lalor massive sulphide deposit case study
Bibliographic record
Abstract
The Lalor deposit in Snow Lake, central Manitoba, is one of the most significant mineral discoveries in Canada in the past decade. Buried 600 m below the surface, the deposit remained undiscovered until a deep penetrating geophysical electromagnetic (EM) system was employed. Since then, the deposit has been a test site for many modern geophysical systems. This paper presents a comparative study of four EM data sets acquired at Lalor. We image the electrical conductivity structure of the subsurface by carrying out independent 3-D inversions of the data. The four data sets are acquired through airborne, surface, and borehole systems, including airborne natural source EM (ZTEM), airborne time-domain EM (HELITEM), surface large loop EM (SQUID), and borehole EM (PULSE-EM). ZTEM has good depth of penetration, but its inversion model may be biased if the background model is not properly chosen. The HELITEM system can complement ZTEM by validating the actual conductivity of the deposit. With the information provided by airborne surveys, surface EM can better define the geometry of the ore body at a local scale and help in defining drilling targets. Once boreholes are drilled, sensors can be sent downhole, possibly probing the ore lenses that are interbedded at a greater depth. Our 3-D imaging experiments demonstrate that modern geophysical technology is capable of making deep exploration and assisting a more informed process throughout the entire workflow from reconnaissance to drilling and development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".