Helicopter Magnetic Field Surveys for Future Mars Missions
Bibliographic record
Abstract
The recent successful flight demonstration of the Mars 2020 helicopter, Ingenuity, has opened doors for future Mars mission concepts that exploit modern technology, and promising investigations include low altitude magnetic field surveys. The martian crustal magnetic field has been studied extensively from orbit and those data sets have allowed global studies of the magnetic field and resulted in a range of models for the crustal magnetic field which however lack short wavelength information that is not resolvable from orbital altitudes. The InSight lander and the Chinese Zhurong missions have recently acquired magnetic measurements of the local field at their respective landing sites. However, to-date no measurements at scales in between those of local surface and global orbital data have been collected. Such measurements are key to understanding near-surface magnetizations, the processes by which they were acquired, and their interaction with magnetic fields generated above the planet’s surface. Here, we investigate data sets that a future helicopter-based magnetometer might be able to provide.We construct forward models that resemble a range of plausible subsurface geological structures that allow us to experiment with survey design, e.g., the value of multiple measurement tracks horizontally and/or vertically and their trade-offs with regional data coverage. We simulate vector magnetic field data collected by a helicopter for different geological scenarios and aim to recover our model via an inverse problem. Because such inverse problems are inherently non-unique, we investigate several approaches to find solutions, including different types of regularization, as well as modification of the model parameterization. As one example, we investigate recovery of a magnetization signature associated with a small (~200 m diameter) crater, from a few (e.g., 3) helicopter tracks over the crater. We show that smooth and sparse inversion solutions result in detection of the signal, with improved localization of the structure in the latter case. Parameterized solutions improve upon sparse solutions, but require some prior knowledge, or assumption, of the geometry (in this case a magnetized half sphere) of the source.Our investigation allows us to assess the capabilities of helicopter-based magnetic field studies in addressing some of the fundamental open questions in the field. These kinds of considerations will greatly aid in preparing for and designing future missions, optimizing their science return and demonstrating their scientific value.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".