WindSightNet: The Inter‐Annual Variability of Martian Winds Retrieved From InSight's Seismic Data With Machine Learning

Abstract Wind measurements from landed missions on Mars are vital to characterize the near surface atmospheric behavior on Mars and improve atmospheric models. These winds are responsible for aeolian change and the mixing of dust in and out of the atmosphere, which has a significant effect on global circulation. The NASA InSight mission recorded wind data for around 750 sols. The seismometer, however, recorded data for around 1400 sols. The dominant source of energy in the seismic data is in fact due to winds. To this end, we propose a machine learning model, dubbed WindSightNet, to map the seismic data to wind speed and direction. The trained network achieves wind speed and direction measurements with errors of 0.932 m/s and 32.6°. We use WindSightNet to retrieve winds from the entire time the seismometer was recording to compare year‐to‐year wind variations at InSight. The continuous nature of the data set enables the extraction of periodic behavior. We observe a pattern of waves due to baroclinic activity with periods of 2–3, 4, 5–7 and 9–20 sols occurring 180–360°. We also observe periodicity during the day due to convective cells. This is used to estimate the boundary layer height, yielding values between 2.3 and 7.7 km. A data‐science based metric is proposed to provide a quantification of the year‐to‐year differences in the wind speeds. This highlights variations linked to dust activity as well as other transient differences. On the whole, the seismic‐derived winds confirm the dominance of the global circulation leading to repeatable weather patterns.

Authors

Institutions

Publication Details

Journal
Journal of Geophysical Research Planets
Published
2025-02-01
DOI
https://doi.org/10.1029/2024je008695
Citations
2
Primary Topic
Planetary Science and Exploration
Type
article
Field-Weighted Citation Impact
2.31

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

WindSightNet: The Inter‐Annual Variability of Martian Winds Retrieved From InSight's Seismic Data With Machine Learning

M. T. Lemmon, Alexander Stott, M. Drilleau, Constantinos Charalambous et al.
2 citations
Journal of Geophysical Research Planets
Planetary Science and Exploration
2.31
article

WindSightNet: The Inter‐Annual Variability of Martian Winds Retrieved From InSight's Seismic Data With Machine Learning

M. T. Lemmon, Alexander Stott, M. Drilleau, Constantinos Charalambous, Aymeric Spiga, R. García, W. B. Banerdt, Claire Newman, W. T. Pike, Luis Mora‐Sotomayor, Sara Navarro, D. Mimoun, D. Banfield, Naomi Murdoch, Philippe Lognonné
article en
2 citations

Abstract

Abstract Wind measurements from landed missions on Mars are vital to characterize the near surface atmospheric behavior on Mars and improve atmospheric models. These winds are responsible for aeolian change and the mixing of dust in and out of the atmosphere, which has a significant effect on global circulation. The NASA InSight mission recorded wind data for around 750 sols. The seismometer, however, recorded data for around 1400 sols. The dominant source of energy in the seismic data is in fact due to winds. To this end, we propose a machine learning model, dubbed WindSightNet, to map the seismic data to wind speed and direction. The trained network achieves wind speed and direction measurements with errors of 0.932 m/s and 32.6°. We use WindSightNet to retrieve winds from the entire time the seismometer was recording to compare year‐to‐year wind variations at InSight. The continuous nature of the data set enables the extraction of periodic behavior. We observe a pattern of waves due to baroclinic activity with periods of 2–3, 4, 5–7 and 9–20 sols occurring 180–360°. We also observe periodicity during the day due to convective cells. This is used to estimate the boundary layer height, yielding values between 2.3 and 7.7 km. A data‐science based metric is proposed to provide a quantification of the year‐to‐year differences in the wind speeds. This highlights variations linked to dust activity as well as other transient differences. On the whole, the seismic‐derived winds confirm the dominance of the global circulation leading to repeatable weather patterns.

Journal of Geophysical Research PlanetsVol. 130(2)
Ames Research Center (US), Centre National de la Recherche Scientifique (FR), Space Science Institute (US), Jet Propulsion Laboratory (US), École Polytechnique (FR), Institut de physique du globe de Paris (FR), Université Fédérale de Toulouse Midi-Pyrénées (FR), Institut Superieur de l'Aeronautique et de l'Espace (ISAE-SUPAERO) (FR), Université Paris Cité (FR), Université Paris Sciences et Lettres (FR), École Normale Supérieure - PSL (FR), Sorbonne Université (FR), Laboratoire de Météorologie Dynamique (FR), Aeolis Research (United States) (US), Centro de Astrobiología (ES), Imperial College London (GB)
UK Space Agency, Agence Nationale de la Recherche, Centre National d’Etudes Spatiales
Affordable and clean energy
Openalex Percentile: Top 13%
Planetary Science and Exploration
2.31
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.