Mineral mapping using spaceborne Tiangong‐1 hyperspectral imagery and ASTER data: A case study of alteration detection in support of regional geological survey at Jintanzi‐Malianquan area, Beishan, Gansu Province, China
Bibliographic record
Abstract
This is an extension of our previous study and an applicability test on the mapping capability of Tiangong‐1 data with more complicated geological conditions over large areas. The Jintanzi‐Malianquan area is located in a major Au‐Cu‐Ni‐Cr resource belts in China. In order to support the 1:50,000 regional geological survey, this study presents the mapping results of using spectral angle mapper method and image endmembers from spaceborne Tiangong‐1 Hyperspectral Imager (HSI) shortwave infrared and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data. Six alteration minerals (muscovite, kaolinite, chlorite, epidote, calcite, and dolomite) related to hydrothermal ore deposits are used in the analysis. By comparing the results from both datasets, it is confirmed Tiangong‐1 HSI data can detect six major minerals (muscovite, kaolinite, chlorite, epidote, calcite, and dolomite), while ASTER can only discriminate the first five minerals in this study area. Fifteen targets for mineral exploration are mapped from the remote sensing results. Eleven targets have been verified by existing geologic maps and field validation for muscovite and epidote alteration. The results of this study suggest that the Tiangong‐1 HSI data are well suited for quick spaceborne reconnaissance of alteration minerals to support routine geological survey in large areas at 20‐m resolution, which provides continuous mapping products for all terrains at an accuracy of better than 1:50,000 scale map.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".