SIT4ME project: Up-scaling seismic methods for mineral exploration in the Zinkgruvan mining area, Sweden
Bibliographic record
Abstract
Mineral resources are used in large quantities than ever before because they are fundamental to our modern society. To this front and facing an up-scaling challenge, the EIT Raw-Materials funded project SIT4ME (Seismic Imaging Techniques for Mineral Exploration) was launched involving several European institutions. As part of the project, a dense multi-method seismic dataset was acquired in the Zinkgruvan mining area at the Bergslagen mineral district of Sweden, which hosts one of the largest volcanic-hosted massive sulphide (VMS) deposits in the country. In November 2018, a dense multi-method seismic dataset was acquired in the Zinkgruvan mining area, in a joint collaborative approach among Swedish, Spanish and German partners. A combination of sparse 3D grid and dense 2D profiles in an area of approximately 6 km2 was acquired using a 32t seismic vibrator (10-150 Hz) of TU Bergakademie Freiberg, enabling reasonable pseudo-3D sub-surface illumination. For the data acquisition, a total of approximately 1300 receiver positions (10-20 m apart), using different recorders, and 950 source positions were surveyed. All receivers were active during the data acquisition allowing a combination of 2D and semi-3D data to be obtained for various imaging and comparative studies. The main objective of the study, apart from its commercial-realization approach, was also to provide information useful for deep-targeting and structural imaging in this complex geological setting. The main massive-sulphide bearing horizon, Zinkgruvan formation, is strongly reflective as correlated with the existing boreholes in the mine. Careful analysis of the seismic sections suggests a dominant northeast-dipping structure, consistent with the general plunge of the main Zinkgruvan fold that has been suggested in the area. Acknowledgements: EIT-RawMaterials is gratefully thanked for funding this up-scaling project 17024.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".