Reduction of edge effects in spatial information extraction from regional geochemical data: A case study based on multifractal filtering technique
Bibliographic record
Abstract
Spatial information extraction in geoscience data analysis often involves operations, such as filtering and reducing noise/signal ratio that are frequently conducted in the frequency domain. Unfortunately, abrupt truncation of data or images along the edges and holes (with missing data) often cause distortion of patterns in the frequency domain. For example, bright strips on the frequency distribution pattern are often seen when the Fourier transform is used. These artifacts due to edge effects may adversely affect the results of data analysis; the effects can be significant depending on edge abruptness. Traditional solutions to reduce edge effects are to smooth the boundary of the image prior to applying the Fourier transformation. Zero-padding is one of the most frequently used smoothing methods. This simple method can reduce the edge effect to some degree but is ineffective in some applications when the image remains distorted. Moreover, due to the complexity of geoscience data involving irregular shapes and holes with missing data, zero-padding generally does not give satisfactory results. In this paper, decay functions are suggested to handle edge effects in geoscience image analysis. As a case study, it is used in a newly developed multifractal filtering technique: spectrum-area fractal method (S-A) for separating geochemical anomalies from background patterns. A geochemical data set chosen from a mineral district in Nova Scotia, Canada was used for validating the method. Optimal parameters including extension width and computational load involved in the selection of decay functions are experimentally determined and documented in this study. © 2004 Elsevier Ltd. All rights reserved.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".