Imaging of sediment-hosted Cu deposits using ambient noise tomography: a case study of the Kansanshi Cu-mine, Zambia.
Bibliographic record
Abstract
Improved passive seismic imaging of sedimentary basins plays a crucial role in improving our understanding of basin inversion tectonics and sedimentary-hosted mineral systems. The Central African Copperbelt of Zambia and the Democratic Republic of Congo is hosted in the Neoproterozoic Katangan sedimentary basin and accounted for 8.8% of global copper production in 2021 (World Economic Forum, 2024). Despite this, the tectonic evolution of the basin in Northern Zambia is currently unclear, significantly hampering our understanding of the Cu, Co and Ni mineralisation in that area. To investigate the geodynamics that shaped this region, and to assess the suitability of MEMS-accelerometers for passive seismic imaging of sedimentary basins, an array of nodal accelerometers was deployed around the Kansanshi Mine (NW Zambia), previously Africa’s largest Cu mine. Surface wave phase velocities in the mine and surrounding area were analysed using ambient noise tomography, with average Rayleigh wave phase velocities ranging from 3.05 +/- 0.2 km/s at 3 s period to 3.5 +/- 0.15 at 6 s period. The S-wave velocity at points of particular interest was examined using iterative non-linear inversions of surface wave dispersion curves constructed from the tomography results. These S-wave profiles provide new insight into the structural configuration of the Kansanshi copper mine and show that the mine overlies a large thickness of sediments from which the copper could be scavenged. This study illustrates the efficacy of performing ambient noise tomography on MEMS-accelerometer data to investigate the structures controlling the inversion of sedimentary basins and the formation of sedimentary-hosted metal deposits at a local to regional scale.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".