Soil anomaly mapping in the Hattfjelldal area, Norway: Reconciling soil geochemical and geophysical properties within their spatial context
Bibliographic record
Abstract
This study presents a multivariate framework for geochemical data processing and anomaly detection to support mineral exploration in the Hattfjelldal area, Norway. The workflow integrates data levelling, multivariate analysis, and spatial evaluation to improve the detection and interpretation of geochemical anomalies associated with volcanogenic massive sulfide (VMS) mineralization. Soil geochemical and magnetic susceptibility data were log-transformed and subsequently levelled using Z -score normalization by soil type and lithology. Both linear (principal component analysis, PCA) and non-linear algorithms (hierarchical clustering, isolation forest, and angle-based outlier detection) were applied to construct anomaly detection vectors. Hierarchical clustering proved particularly effective in defining mineral assemblages that refine anomaly detection, including associations of Type 1 (Ag, Mo, S, Sb, Bi, Pb); Type 2 A (Fe, Zn, Co, Mn) and Type 2B (Fe, Zn, Co, Mn, As, Cu). These groupings provide a robust geochemical and geological context within established VMS zoning models. Magnetic susceptibility, although less reliable as a stand-alone exploration vector, enhances interpretation when integrated with geochemical anomalies. Fractal analysis applied to both, geochemical vectors and magnetic susceptibility data effectively distinguished background from anomalous values, delineating areas of potential economic interest. Spatial Feature Embeddings (SFE), derived from clustering radiometric, topographic, and spectral datasets, further improved the spatial characterization of anomalies. When combined with airborne magnetics, SFE enabled the refinement and prioritization of specific targets within broad anomaly zones. Overall, this framework demonstrates the value of integrating statistical, geochemical, and geophysical methods within their spatial context, providing a transferable approach for exploration programs in Arctic environments. • CoBA outperforms PCA in detecting geochemical anomalies. • Fractal models separate background from mineralized zones. • SFE maps enhance spatial context in mineral exploration. • Integrated data reveal VMS targets in Hattfjelldal, Norway.
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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 | 0.001 |
| 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 | 0.000 | 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".