A review of high-resolution seismic profiling across the Sudbury, Selbaie, Noranda, and Matagami mining camps
Bibliographic record
Abstract
Lithoprobe high-resolution seismic surveys have provided the first systematic images of the deep stratigraphy in four major Canadian mining camps (Noranda, Matagami, Sudbury, and Selbaie). Systematic compressional wave velocity and density measurements in deep boreholes have established that lithological contacts were the main impedance contrast imaged, although reflections from faults and deformation zones have also been observed. The strongest reflections are attributed to mafic intrusions and some sulphides and oxides. Integrating seismic, physical rock property measurements, and geological data has resulted in the revision of several geological models with direct impact on local strategies for deep mineral exploration. Mining companies have shown an interest in seismic reflection methods and this has led to several follow-up studies. The application of seismic methods to the direct detection of massive sulphides, based on physical rock property measurements, has been studied through two-dimensional and three-dimensional (3D) seismic imaging and vertical seismic profiling technologies. The challenge will now be to optimize 3D seismic imaging for mineral exploration and to improve seismic data processing by enhancing the seismic response from deep, lenticular orebodies.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".