An analytical study on the effect of inverse prediction based on two types of convolutional neural networks NestU-Net and ResU-Net++
Bibliographic record
Abstract
Exploring new methods for gravity anomaly inversion to quickly and accurately understand the distribution and physical properties of subsurface space is the focus of current research. In this paper, we analyse the significant differences in the prediction results of different network structures for the same subsurface information from machine learning, and highlight the differences in the theoretical model prediction recovery effects of four U-Net networks (AttU-Net, NestU-Net, R2U-Net, ResU-Net++). Two of the more effective networks, NestU-Net and ResU-Net++, were selected and applied to 3D density imaging of the Mobrun sulphide ore body in Noranda, Quebec, Canada. By combining the previous inversion results and comparing the prediction effect of the two inversions, it is concluded that ResU-Net++ is more effective than NestU-Net network application in the work zone of sulphide ore body, and it also provides a new theoretical and methodological support for the prediction and restoration of the geophysical actual subsurface geological information.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".