Mapping Lava Flows on Venus Using SAR and InSAR: Hawaiʻi Case Study
Bibliographic record
Abstract
Abstract We explore the potential for repeat‐pass SAR Interferometry (InSAR) correlation to track volcanic activity on Venus' surface motivated by future SAR missions to Earth's sister planet. We use Hawai'i as a natural laboratory to test whether InSAR can detect lava flows assuming orbital and instrument parameters similar to that of a Venus mission. Hawai'i was chosen because lava flows are frequent, and well documented by the United States Geological Survey, and because Hawai'i is a SAR supersite, where space agencies have offered open radar data sets for analysis. These data sets have different wavelengths (L, C, and X bands), bandwidths, polarizations, look angles, and a variety of orbital baselines, giving opportunity to assess the suitability of parameters for detecting lava flows. We analyze data from ALOS‐2 (L‐band), Sentinel‐1 (C‐band), and COSMO‐SkyMed (X‐band) spanning 2018 and 2022. We perform SAR amplitude and InSAR correlation analysis over temporal baselines and perpendicular baselines similar to those of a Venus mission. Fresh lava flows create a sharp, noticeable decrease in InSAR correlation that persists indefinitely for images spanning the event. The same lava flows are not always visible in the corresponding amplitude images. Moreover, noticeable decorrelation persists in image pairs acquired months after the events due to post‐emplacement contraction of flows. Post‐emplacement effects are hypothesized to last longer on the Venusian surface, increasing the likelihood of detecting Venus lava flows using InSAR. We argue for further focus on repeat‐pass InSAR capabilities in upcoming Venus missions, to detect and quantify volcanic activity on Earth's hotter twin.
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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 | 0.001 |
| 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 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".