Nature-inspired stochastic hybrid technique for joint and individual inversion of DC and MT data
Bibliographic record
Abstract
Here, a new naturally-inspired stochastic nonlinear joint and individual inversion technique for integrating direct current (DC) and magnetotelluric (MT) data interpretation-based simulation of a swarm intelligence combo with specific capabilities for exploitation of the variable weight particle swarm optimizer (vPSO) and exploration of the grey wolf optimizer (GWO), vPSOGWO, is used. They are particularly notable for their capacity for information exchange while hunting for food. Through synthetic MT and DC data contaminated with various sets of random noise, the applicability of the anticipated vPSOGWO algorithm based joint and individual inversion algorithm was assessed. The field examples, collected from diversified different geological terrains, including Digha (West Bengal), India; Sundar Pahari (Jharkhand), India; Puga Valley (Ladakh), India; New Brunswick, Canada; and South Central Australia, have shown the practical application of the proposed algorithm. Further, a Bayesian probability density function (bpdf) for estimating a mean global model and uncertainty assessment in posterior; and a histogram for model resolution assessment have also been created using 1000 inverted models. We examined the inverted outcomes and compared them with results from other cutting-edge methodologies, including the GWO, PSO, genetic algorithm (GA), Levenberg-Marquardt (LM), and ridge-regression (RR). Our findings showed that the current methodology is more effective than the GWO, PSO, GA, LM, and RR techniques at consistently improving the convergence of the global minimum. In contrast to earlier approaches, the current cutting-edge strategy vPSOGWO offers an improved resolution of an additional significant crustal thickness of about 65.68 ± 1.96 km over the Puga Valley, in which the inverted crustal thickness determined by vPSOGWO agrees well with the published crustal thickness over the Puga Valley. The new technology brings simulations closer to genuine models by significantly reducing uncertainty and enhancing model resolution.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".