SPATIAL SELF-SIMILARITY AND GEOPHYSICL AND GEOCHEMICAL ANOMALY DECOMPOSITION
Bibliographic record
Abstract
Anomaly separation must be necessary for processing geophysical and geochemical data for mineral exploration. The objective of this task is to decompose geophysical and geochemical fields into distinct components to reflect different geological entities and to study their related geological processes such as mineralization and alteration. The geological bodies created from the same geological process may not be always corresponding to the same field values and frequency characteristics. The variations of the field patterns are also related to the geometries of the geological bodies, their existing depth below the earth surface and the relative differences between them and their neighbourhoods. However, due to the multiple phases and spatial associations of most of the geological processes especially mineralization, the geological entities caused and their related fields are often of self similarity or self affinity. These self similarity or self affinity can be employed to assist geological anomaly recognition. A multifractal approach (S A method) introduced in the current paper defines irregular filters in frequency domain based on the distinctive self similarity of power spectra. It has been demonstrated with a number of case studies including analysis of gamma ray spectrometer data U Th K in the southwestern Nova Scotia, Canada that the S A method is an effective technique for identifying mineralization related geophysical and geochemical anomalies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".