Details and preliminary positive evaluation of a test seismic interferometry survey at an active VMS mine near Snow Lake, Manitoba
Bibliographic record
Abstract
Seismic reflections methods are a powerful tool to detect and image structures associated with volcanogenic massive sulphide (VMS) deposits. Seismic interferometry has recently been developed as a robust method to process passive seismic data and image geological features. In order to test the capability of seismic interferometry to image ore deposits in the crystalline rock environment approximately 300 hours of ambient noise data covering an area of 4 km² were acquired over the Lalor mining area, near Snow Lake, MB, Canada,. The interferometry survey consisted of 336 receivers installed in a grid comprising sixteen lines. The study area encompasses the Lalor deposit, a 27 Mt VMS deposit located at a depth of ~700 m. A distinct, overlapping 3D active source seismic survey was also acquired in the area and we use it here to evaluate our interferometry results. An estimate of the seismic wave field (Green's function) is retrieved by crosscorrelating the noise between all receiver locations in each hourly segment of passive seismic data. The crosscorrelated results are summed to generate 'virtual' shot gathers at each physical receiver location. The virtual data is processed along all 2D lines with conventional methods similar to those applied to active 3D data. The DMO-stacked section obtained reveals a number of events, some more coherent than observed on the active seismic section. Of particular interest is an event possibly associated with one of the lenses associated with the massive sulphide deposit. A comparable event is also observed on the active seismic data. These results are encouraging and demonstrate the benefits of ambient noise measurements and interferometry in for mineral exploration in crystalline rock environment.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".