Efficient base metal exploration in northern New Brunswick, Canada through a hybrid ANN integrated with ABC and PSO methods
Bibliographic record
Abstract
This study investigates the application of a hybrid neural network model for delineating sulfide mineralization in complex geological settings. By integrating Artificial Neural Networks (ANN) with metaheuristic algorithms, specifically Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO), the research optimizes resistivity inversion techniques to enhance geophysical exploration. Field data were collected from the Nash Creek region in New Brunswick, Canada, utilizing electromagnetic (EM) surveys and Direct Current/Induced Polarization (DC/IP) methods across six survey lines configured with a Wenner array and 10-m electrode spacing. The results demonstrate that the hybrid model significantly outperforms traditional 2D resistivity inversion methods, effectively mapping mineralization zones characterized by low resistivity (< 50 Ω·m) and high chargeability Because the region is characterized by a significant glacial overburden and minimal outcrop exposure, verification was conducted using borehole drilling and core sampling within the western part of the study area. Notably, pyrite alteration zones were identified in regions with resistivity below 50 Ω·m, exhibiting significant chargeability. Strong correlations between Versatile Time-Domain Electromagnetic (VTEM) survey results and IP data were observed, with Transient Electromagnetic (TEM) Reduced to Pole (RTP) data aligning closely with resistivity measurements. This study underscores a significant inverse correlation between resistivity and chargeability, effectively identifying pyrite-rich alteration zones. These findings validate the efficacy of the hybrid ANN-ABC-PSO model for geophysical exploration in challenging environments, offering valuable insights into mineral deposit structures and informing future exploration strategies. While shallow alteration zones were successfully mapped, further drilling is essential to confirm the extent and characteristics of the identified mineralization.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".