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Stott, Alexander; Murdoch, Naomi; Gillier, Martin; Banfield, Don; Bertrand, Tanguy; Chide, Baptiste; De la Torre Juarez, Manuel; Hueso, Ricardo; Lorenz, Ralph; Martinez, German; Munguira, Asier; Mora Sotomayor, Luis; Navarro, Sara; Newman, Claire; Pilleri, Paolo; Pla-Garcia, Jorge; Randazzo, Nicolas; Rodriguez Manfredi, Jose Antonio; Sanchez-Lavega, Agustin; Smith, Michael; Viudez Moreiras, Daniel; Williams, Nathan; Maurice, Sylvestre; Wiens, Roger;

Martian Wind and turbulence heard by the SuperCam microphone on the perseverance rover

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  • Media type: E-Article
  • Title: Martian Wind and turbulence heard by the SuperCam microphone on the perseverance rover
  • Contributor: Stott, Alexander; Murdoch, Naomi; Gillier, Martin; Banfield, Don; Bertrand, Tanguy; Chide, Baptiste; De la Torre Juarez, Manuel; Hueso, Ricardo; Lorenz, Ralph; Martinez, German; Munguira, Asier; Mora Sotomayor, Luis; Navarro, Sara; Newman, Claire; Pilleri, Paolo; Pla-Garcia, Jorge; Randazzo, Nicolas; Rodriguez Manfredi, Jose Antonio; Sanchez-Lavega, Agustin; Smith, Michael; Viudez Moreiras, Daniel; Williams, Nathan; Maurice, Sylvestre; Wiens, Roger;
  • imprint: Acoustical Society of America (ASA), 2023
  • Published in: The Journal of the Acoustical Society of America
  • Language: English
  • DOI: 10.1121/10.0018841
  • ISSN: 0001-4966; 1520-8524
  • Keywords: Acoustics and Ultrasonics ; Arts and Humanities (miscellaneous)
  • Origination:
  • Footnote:
  • Description: <jats:p>On top of listening to laser shots, rover sounds and the Ingenuity rotorcraft, SuperCam’s Mars microphone has recorded over 7 hours of ambient background noise on Mars. These background recordings contain signal due to the Martian wind. Through a comparison to the meteorological data recorded by the MEDA (Mars Environmental Dynamics Analyzer), we can determine the relationships between the microphone data, the wind and the atmospheric stability. Based on these relationships, we have determined a way to estimate the wind speed using the microphone through Gaussian process regression, a machine learning technique. Owing to the sampling rate of 25 000 samples per second, the microphone data can be used to examine Mars’ atmospheric dynamics at high frequencies, as yet unexplored on Mars. We will demonstrate how the wind speed estimates from the microphone provide an assessment of turbulence at fine scales, shedding light on the dissipative regime on Mars. One particularly interesting signal recorded by the microphone was a dust devil, which had fast varying winds within the walls of its vortex and signal from dust particles hitting the rover. Combining the microphone data with information from the MEDA sensors and navigation camera (Navcam) images enabled a full parameterization of this event.</jats:p>