Detection of cryptotephra in sedimentary profiles using reflectance spectroscopy
Bibliographic record
Abstract
Explosive volcanic eruptions often blanket landscapes with tephra deposits that, if found, serve as valuable geochronological markers for landforms and sedimentary archives. However, fine-grained tephra (ash) layers are commonly obscured and go undetected, especially when ash fallout from smaller or more distant eruptions is thin, or where tephra has been mixed into host sediments by post-depositional reworking. Detecting these invisible trace tephras, termed ‘cryptotephra’, can greatly expand the scope of tephrochronology in geomorphological and stratigraphic investigations. Here, we use reflectance spectroscopy to detect cryptotephra within sedimentary landforms in western Canada. We first experimentally determine the visible to short-wave infrared (VSWIR) reflectance patterns of field-derived tephra and host sediments from alluvial, glacial, paleosol, and aeolian deposits. These data are used to build a tephra-detection model based on key absorption features principally arising from hydrated, Fe-bearing glass shards in tephra. Sensitivity analyses indicate that cryptotephra concentrations as low as 16 wt% can be confidently distinguished from host sediments characteristic of many post-glacial landforms in western Canada. Tephra concentration profiles from two field outcrops at Abraham Lake, Alberta, reveal an otherwise-indistinguishable cryptotephra (17 wt%) from the Mount St. Helens Yn eruption, along with evidence of syn - and post-depositional mixing. To test reproducibility, we apply the model to a tephra-bearing alluvial fan in northwest Argentina, where we again detect reworked cryptotephra within an incised fan section. Our findings demonstrate that field-based reflectance spectroscopy can (i) rapidly screen for cryptotephra in sediments and landforms; (ii) quantify tephra abundance in mixed or reworked deposits; and (iii) facilitate more-detailed terrestrial tephrochronology than traditional approaches.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 teacher head, 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".