High-frequency passive surface wave tomography at the Marathon PGE-Cu deposit (Ontario, Canada): Bridging the gap between natural and anthropogenic noise sources
Bibliographic record
Abstract
Ambient noise surface wave tomography is an environmentally friendly and cost-effective seismic technique for subsurface imaging. However, noise sources acting from preferential azimuths may introduce bias in the Green’s function reconstruction and in the resultant velocity models. This study, focused at the deposit scale, investigates how to correctly merge the different phase velocity measurements at various frequencies, in order to fill the gap between natural and anthropogenic noise sources while adjusting the bias caused by changes in the azimuth of the source. The target is the Marathon PGE-Cu deposit (Ontario, Canada), an alkaline intrusion containing gabbros and syenites (ø = 25 km). Mineralisation is hosted by gabbros close to the inward-dipping footwall of the intrusion. The country rocks are Archaean volcanic breccias. 1024 vertical-component receivers were deployed for 30 days in two overlapping grids: a 200 m x 6040 m dense array with node spacing of 50 m, and a 4000 m x 2500 m sparse array with node spacing of 150 m. Beamforming analysis of the recorded data indicates variations in the distribution of noise. Below 5 Hz, the Lake Superior (SSW) is the dominant source of noise, while above 12 Hz, noise from the Canadian Pacific Railway and Trans-Canada highway (SW) is prominent. In the 5 - 12 Hz frequency band, surface-wave energy is dominant, and it comes from the Lake Superior and vehicle traffic. Between 12 Hz and 20 Hz, the signal is characterized by body-wave energy combined with less energetic surface waves, while above 20 Hz the imprint of body waves is dominant. We retrieved the fundamental mode of Rayleigh wave propagation from the recorded data set. The signal was down-sampled to 50 Hz, divided into segments of 30 minutes, cross-correlated and stacked. Surface wave dispersion curves were extracted from 2-km-long arrays. Besides, various phase velocity measurements were applied. Phase-velocities were inverted to S-wave velocity structures using different probabilistic approaches. The overall results show a high-velocity shallow anomaly, probably related to the gabbro intrusion hosting the mineralization, as well as other structures consistent with the geological model inferred from surface mapping and drill logs.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".