Global optimization of self-potential anomalies via HGS algorithm
Bibliographic record
Abstract
In this paper, a novel global optimizer named Hunger Games Search (HGS) is adapted and presented for estimating the characteristics and model parameters of idealized geometrical causative structures from self-potential (SP) anomalies. After the modal analysis of assumed objective functions and the essential parameter tuning process, the HGS algorithm has been tested on some synthetic SP anomalies and four heterologous real data cases obtained from India, Türkiye, Canada, and Indonesia. In the synthetic and real data cases, multiple intercalating structures have been considered. The second moving average method has also been tested for removing the regional background. Additionally, the post-inversion uncertainty appraisal analyses have been implemented to better understand the reliability of the model parameter estimations. The solutions obtained through HGS have been systematically and thoroughly compared with the solutions of the well-established and popular Particle Swarm Optimization (PSO) in terms of convergence rate, robustness, stability, and accuracy. Applications have shown that SP anomalies can be inverted more effectively via HGS since the algorithm can explore the search space more extensively without being trapped into numerous local minima and approximate the global minimum more precisely. Thus, it is recommended to use the HGS algorithm in ore and mineral exploration studies as well as in studies aimed at delineating subsurface structures from geophysical data sets.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".