Cross-profile seismic data acquisition, imaging, and modeling of iron-oxide deposits: A case study from Blötberget, south-central Sweden
Bibliographic record
Abstract
Abstract Two 2D reflection seismic profiles were acquired in Blötberget, south-central Sweden, for deep targeting and delineation of sheet-like iron-oxide deposits, known to dip toward the southeast and extend down to at least 0.8 km depth from core drilling observations. The two perpendicular profiles recorded shots at every receiver station along the main and cross profiles. To obtain more information on the lateral extent of the mineralized horizons, data from the two profiles, including the cross-profile records, were binned together in a 3D grid and further processed as a 3D data set. Processing results suggest that more information is retrieved when 3D processing is used and the mineralization lateral extent can be inferred for at least 0.3 km. The seismic response of the mineralization was further studied through forward reflection traveltime modeling, using a 3D ray-tracing approach; thus, the 3D geometry of several planar reflectors was validated. Additionally, 2D elastic finite-difference modeling work showed that the observed reflection pattern in the seismic data may originate from several mineralized horizons, suggesting potential resources in the footwall of the known deposits and large-scale geologic structures. The results encourage the use of seismic methods for direct delineation of mineral deposits even from 2D profiles and prompted a 3D survey in the area.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".