Differentiating Fissure‐Fed Lava Flow Types and Facies Using RADAR and LiDAR: An Example From the 2014–2015 Holuhraun Lava Flow‐Field
Bibliographic record
Abstract
Abstract Distinguishing between lava types and facies using remote sensing data is important for interpreting the emplacement history of lava flow‐fields on Earth and other planetary bodies. Lava facies typically include a mixture of lava types and record the collective emplacement history of material preserved at a particular location. We seek to determine if lava facies in the 2014–2015 Holuhraun lava flow‐field are discernible using radar roughness analysis. Furthermore, we also seek to distinguish between lava types using high resolution Light Detection and Ranging (LiDAR) data. We extracted circular polarization ratios (CPR) from the Uninhabited Aerial Vehicle Synthetic Aperture Radar and cross‐polarization (VH/VV) data from the Sentinel‐1 satellite to analyze the surface roughness of three previously mapped lava facies: rubbly, spiny, and undifferentiated rubbly–spiny. Using the Kruskal‐Wallis test, we reveal that all but one pair of the facies are statistically separable. However, the populations overlap by 88%–89% for CPR and 64%–67% for VH/VV. Therefore, owing to large sample populations (n > 2 × 105), slight differences in radar data may be used to probabilistically infer the presence of a particular facies, but not directly map them. We also calculated the root‐mean‐square slope and Hurst exponents of five different lava types using LiDAR topography (5 cm/pixel). Our results show minute differences between most of the lava types, with the exception of the rubbly pāhoehoe, which is discernible at 1σ. In brief, the presence of “transitional” lava types (e.g., rubbly pāhoehoe) within fissure‐fed lava flow‐fields complicates remote sensing‐based mapping.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".