Effective methods to highlight and delineate anomalies from geophysical images
Bibliographic record
Abstract
Geophysical data interpretation is largely an anomaly detection task which involves recognising and synthesising anomalous patterns within single or multiple datasets. The accuracy and efficiency of these interpretations heavily relies on the skills and practices of interpreters, thus the greatest challenge is to minimise personal biases to produce objective and consistent interpretation outcomes. We present an innovative data visualisation method which can empower interpreters to effectively delineate anomalies of varying frequency scales within aeromagentic data using a single image display. This is achieved by harnessing the power of image enhancement and visualisation techniques to assist interpretation.We adapted and extended the use of colour composite techniques to present different frequencies presented in potential field data. Aeromagnetic data from an area in Kirkland Lake, Ontario, Canada is used for our experiment. long wavelength and short wavelength anomalies are identified from the data using low pass- and high pass filters respectively. These two different frequency enhanced images and the original image are represented as separate colour channels which are then combined to generate a composite image. The luminance of the composite image is scaled to highlight high frequency signals as they hold the key for detailed structure interpretation. We use a technique called dynamic range compression, which preserves the integrity of the phase component of the signal while performing high pass filtering. The resulting display is compared to the geological map of the area to validate the effectiveness of the method. The proposed technique is widely adaptable for different types of datasets.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".