Reliable and efficient magnetic data inversion for resource detection using a hybrid bat algorithm
Bibliographic record
Abstract
Accurate estimation of subsurface parameters is a critical objective in geophysical exploration. This study introduces an innovative hybrid algorithm integrating the Bat Algorithm (BA) with the Fourth Horizontal Gradient (FHG) to optimize the estimation of geometric parameters (depth, amplitude coefficient, shape factor, source origin, and magnetization angle) from magnetic field data. The FHG enhances the resolution of magnetic anomalies by mitigating regional field effects, while the BA efficiently navigates the parameter space to accurately delineate subsurface structures modeled as simplified geometric. This approach prioritize geometric parameters to define the spatial configuration of magnetic sources, assuming constant petrophysical properties (e.g., magnetization intensity, susceptibility contrast) to simplify the inversion process. The algorithm's robustness and effectiveness were extensively evaluated using synthetic magnetic datasets under both noise-free and with 10% Gaussian noise conditions. The findings demonstrate the method's capability to achieve accurate parameter estimation even in noisy environments. The proposed approach was further assessed using real magnetic profile data acquired from the Faro Mine Complex in Yukon, Canada. The estimated subsurface parameters closely match with well data and prior studies, emphasizing the algorithm's practical effectiveness. This integrated approach significantly advances the interpretation of magnetic datasets by improving both the accuracy and resolution of subsurface parameters estimation within the framework of idealized geometric models.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".