Surface Properties of Sediments at the 2014–2015 Holuhraun Lava Flow-field: Insights from Multiwavelength Radar
Bibliographic record
Abstract
Abstract Redistribution of sediments can lead to mantling of geologic features. This can obscure the original texture of lava flows, making it difficult to understand their emplacement conditions based on remote sensing data alone. In situ data acquired in planetary-analog environments can be used with remote sensing data to estimate radar attenuation due to mantling and improve interpretations of planetary data sets. We use remote sensing data of the 2014–2015 Holuhraun lava flow-field in Iceland to quantify how sediment mantling impacts radar returns. Specifically, we (1) examine changes between 2015 and 2022 in Sentinel-1 C-band (5.405 GHz) synthetic aperture radar data due to sediment mantling over the flow-field, (2) use in situ ground-penetrating radar (GPR) measurements to estimate sediment thickness, and (3) incorporate radar modeling work to estimate attenuation for both C- and L-band radar and surface scattering. Our results show that lava mantled by sediment exhibits a reduction in radar backscatter on the order of ∼4 dB from 2015 to 2022 for VH and VV polarizations. Field work in 2022 July using GPR resolved average sediment layer thicknesses of 37–62 cm for two locations along the northern margin of the 2014–2015 Holuhraun lava flow-field. We suggest that damp sediment impedes radar penetration and that radar loss from 2015 to 2022 is due to surface scattering differences between rough lava and a smooth sediment-mantled surface. This highlights the importance of constraining surface dielectric properties for interpretation of future planetary radar data sets and modeling work.
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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 | 0.001 | 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 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".