What can machine learning do for geomagnetic data processing? A reconstruction application
Bibliographic record
Abstract
The integrity of geomagnetic data is a critical factor for understanding the evolutionary process of Earth's magnetic field, as it can provide useful information for near-surface exploration, unexploded explosive ordnance (UXO) detection, etc. Aimed to reconstruct geomagnetic data from under-sampled or missing traces, this paper presented an approach based on machine learning techniques to avoid the time & labor-intensive nature of the traditional manual and linear interpolation approaches. In this study, three classic machine learning models, support vector machine (SVM), random forests and gradient boosting were built. The proposed learning models were first used to specify a continuous regression hyperplane from training data, to recognize the probably intrinsic relation between missing and completed traces. Afterwards, the trained models were used to reconstruct the missing geomagnetic traces for validation, while testing other new field data. Finally, numerical experiments were derived. The results showed that the performance of our methods was more competitive in comparison with the traditional linear method, as the reconstruction accuracy was increased by approximately 10% ~ 15%.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".