Joint and Individual inversion of DC and MT datasets based on Variable Particle Swarm-Grey Wolf Optimizer and Bayesian Statistical Approach
Bibliographic record
Abstract
Abstract In this paper, a new stochastic technique known as variable Particle Swarm - Grey Wolf Optimizer (vPSOGWO), a combination of metaheuristic algorithms based swarm intelligence with distinct capacities for exploration and exploitation is employed. To evaluate the efficacy of the hybrid algorithm, joint and individual inversion of various amount of synthetic datasets and finally applied on field example over various geological terrains namely for individual inversion of DC data from Digha, India and New Brunswick of Canada; MT sounding data from Sundar Pahari, Dhanbad and Puga valley, Ladakh of India and for joint inversion of DC and MT sounding data from South Central Australia. Furthermore, a posterior Bayesian probability density function using 1000 models has been computed to estimate a mean global model and uncertainty assessment. We examined the inverted results, which indicate that the results of the vPSOGWO have been shown to be more accurate than those of the PSO, GWO, and state-of-the-art variant of classic approaches. Additionally, geological significance of crustal thickness of approximately 76.581.96 km was resolved over Puga-valley, India, and is in good agreement with published data. As a result, the new approach greatly reduces uncertainty and enhances model resolution, bringing them closer to actual 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 | 0.001 |
| 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 | 0.000 |
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".