Spaceborne visible and thermal infrared lithologic mapping of impact-exposed subsurface lithologies at the Haughton impact structure, Devon Island, Canadian High Arctic: Applications to Mars
Bibliographic record
Abstract
Abstract— This study serves as a proof-of-concept for the technique of using visible-near infrared (VNIR), short-wavelength infrared (SWIR), and thermal infrared (TIR) spectroscopic observations to map impact-exposed subsurface lithologies and stratigraphy on Earth or Mars. The topmost layer, three subsurface layers and undisturbed outcrops of the target sequence exposed just 10 km to the northeast of the 23 km diameter Haughton impact structure (Devon Island, Nunavut, Canada) were mapped as distinct spectral units using Landsat 7 ETM+ (VNIR/SWIR) and ASTER (VNIR/SWIR/TIR) multispectral images. Spectral mapping was accomplished by using standard image contrast-stretching algorithms. Both spectral matching and deconvolution algorithms were applied to image-derived ASTER TIR emissivity spectra using spectra from a library of laboratory-measured spectra of minerals (Arizona State University) and whole-rocks (Ward's). These identifications were made without the use of a priori knowledge from the field (i.e., a “blind” analysis). The results from this analysis suggest a sequence of dolomitic rock (in the crater rim), limestone (wall), gypsum-rich carbonate (floor), and limestone again (central uplift). These matched compositions agree with the lithologic units and the pre-impact stratigraphic sequence as mapped during recent field studies of the Haughton impact structure by Osinski et al. (2005a). Further conformation of the identity of image-derived spectra was confirmed by matching these spectra with laboratory-measured spectra of samples collected from Haughton. The results from the “blind” remote sensing methods used here suggest that these techniques can also be used to understand subsurface lithologies on Mars, where ground truth knowledge may not be generally available.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".