One-Dimensional-Mixed Convolution Neural Network and Covariance Pooling Model for Mineral Mapping of Porphyry Copper Deposit Using PRISMA Hyperspectral Data
Bibliographic record
Abstract
Mapping distribution of alterations around porphyry copper deposits (PCDs) greatly affects mineral exploration. Diverse geological processes generate irregular alteration patterns with diverse spectral characteristics in mineral deposits. Applying remotely sensed hyperspectral images (HSIs) is an appealing technology for geologic surveyors to generate alteration maps. Conventional methods mainly use shallow spectral absorption features to discriminate minerals and cannot extract their important spectral information. Deep neural networks with nonlinear layers can evoke the deep spectral and spatial information of HSIs. Deep learning-based methods include fully connected neural networks, convolutional neural networks, and hybrid convolutional networks like mixed convolution neural network and covariance pooling (MCNN‐CP) algorithms. However, each has its advantages and limitations. To significantly avoid losing important spectral features, we proposed a new method by fusing a one‐dimensional convolutional neural network (1D‐CNN) with MCNN‐CP (1D‐MCNN‐CP), achieving an overall accuracy (97.44%) of mineral mapping from PRISMA HSIs. This research deduced that 1D‐MCNN‐CP improved performance and reduced misclassification errors among minerals sharing similar spectral features.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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".