Electromagnetic Sounding on Mars with InSight
Bibliographic record
Abstract
The NASA InSight mission operated on Mars from November 2018 to May 2022, primarily aimed at investigating the planet’s interior structure using seismology, geodesy, and heat flow measurements. Among its instruments was the InSight Fluxgate magnetometer, IFG, part of an auxiliary sensor suite, which provided environmental monitoring data. The IFG captured the first surface measurements of Mars’ crustal magnetic field, as well as time-varying magnetic fields. These data enable electromagnetic (EM) sounding, a technique that uses interactions between time-varying magnetic fields and the subsurface to infer electrical conductivity. Electrical conductivity is in turn linked to subsurface mineralogy, temperature, and volatile content, offering complementary insights to other geophysical methods.Previous attempts to use InSight IFG data for EM sounding were unsuccessful due to contamination from spacecraft-generated signals and limited data coverage. Here, we report the first successful EM sounding results from InSight data. By focusing on time periods of 100–1000 seconds, where coherence between horizontal and vertical magnetic field components is high, we compute transfer functions. Further, we derive the corresponding C-response under the assumption of an inducing field geometry and invert those for electrical subsurface elctrical conductivity.Because the largest scale inducing field detectable at the equator (n=m=1) provides a maximum penetration depth and a lower limit of crustal conductivity, we evaluate the effect of a range of inducing field geometries. Irrespective of inducing field geometry, our results reveal conductivity profiles, characterized by a high-conductivity crust (>~10⁻² S/m) underlain by more resistive material. This contrasts with expectations of a cold, dry Martian crust and suggests elevated volatile content, high iron concentrations, and / or increased temperatures.Our findings demonstrate the utility of EM sounding on Mars and underscore the scientific potential of magnetometer data in planetary exploration. They also highlight the need for further investigation of Martian electrical conductivity at longer periods and therfore at larger depths, which may reveal new insights into the planet’s thermal evolution and volatile inventory.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".