A fractal filtering technique for processing regional geochemical maps for mineral exploration
Bibliographic record
Abstract
A regional geochemical map interpolated from point data, usually sampled in surficial media such as stream sediments or lake sediments, may contain a large amount of information critical for mineral exploration and environmental studies. The geochemical map is, however, not ‘ready-to-use’ for such tasks as the determination of a local ‘anomaly’ or the characterization of a regional trend of one or more chemical elements as may be required for the purpose of mineral resource prediction. This becomes possible only after the map has been clearly divided into different components. Fractal filtering, a recently developed technique for decomposing a map or image into different components, helps to separate the anomaly from background or to extract other meaningful patterns from the geochemical map using both frequency and spatial information. The fractal filters are formed by applying the fractal concentration-area model to the power spectrum of the processed geochemical field. They often constitute a group of irregularly shaped filters in the frequency domain that can separate the domain of wave numbers into distinct regions, each with a power spectrum following a similar power-law or fractal property. The corresponding patterns of the separate components are obtained after transformation back to the spatial domain. The fractal filter can be applied to decompose the original geochemical field into a set of map components with distinct scaling ranges and anisotropy. The analysis of relationships among these decomposed maps can provide useful information for the interpretation and evaluation of anomalies or trends. This paper briefly introduces the theory behind the fractal filtering technique. A case study of regional geochemical data of lake sediments from western Meguma Terrain, Southern Nova Scotia, Canada, is used to illustrate application of this technique to process the regional geochemical maps of the study area for the prediction of the turbidite-hosted gold deposits.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".