Why 3D seismic data are an asset for exploration and mine planning? Velocity tomography of weakness zones in the Kevitsa Ni-Cu-PGE mine, northern Finland
Bibliographic record
Abstract
ABSTRACT Kevitsa is a disseminated Ni-Cu-PGE (platinum group elements) ore body in northern Finland, hosted by an extremely high-velocity (6.5–8.5 km/s) ultramafic intrusion. It is currently being mined at a depth of approximately 100 m with open-pit mining. The estimated mine life is 20 years, with the final pit reaching a depth of 500–600 m. Based on a series of 2D seismic surveys and given the expected mine life, a high-resolution 3D seismic survey was justified and conducted in the winter of 2010. We evaluate earlier 3D reflection data processing results and complement that by the results of 3D first-arrival traveltime tomography. The combined results provide insights on the nature of some of the reflectors within the intrusion. In particular, a major discontinuity, a weakness zone, is delineated in the tomography results on the northern side of the planned pit. Supported by the reflection data, we estimate the discontinuity, likely a thrust sheet, to extend down approximately 600 m and laterally 1000 m. The weakness zone terminates prominent internal reflectivity of the Kevitsa intrusion, and it is associated with the extent of the economic mineralization. Together with other weakness zones, a couple of which are also revealed by the tomography study, the discontinuity forms a major wedge block that influences the mine bench stability on the northern side of the open pit and likely will cause more issues during the extraction of the ore in this part of the mine. We argue that 3D seismic data should routinely be acquired prior to commencement of mining activities to maximize exploration efficiency at depth and also to optimize mining as it continues toward depth. Three-dimensional seismic data over mineral exploration areas are valuable and can be revisited for different purposes but are difficult to impossible to acquire after mining has commenced.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".