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Anatomy of continuous Mars SEIS and pressure data from unsupervised learning

Authors
  • Barkaoui, Salma
  • Lognonné, Philippe
  • Kawamura, Taichi
  • Stutzmann, Éléonore
  • Seydoux, Léonard
  • de Hoop, Maarten
  • Balestriero, Randall
  • Scholz, John-Robert
  • Sainton, Grégory
  • Plasman, Matthieu
  • Ceylan, Savas
  • Clinton, John
  • Spiga, Aymeric
  • Widmer-Schnidrig, Rudolf
  • Civilini, Francesco
  • Banerdt, W. Bruce
Publication Date
Nov 09, 2021
Identifiers
DOI: 10.1785/0120210095
OAI: oai:HAL:hal-03532354v1
Source
HAL
Keywords
Language
English
License
Unknown
External links

Abstract

The seismic noise recorded by the Interior Exploration using Seismic Investigations, Geodesy, and Heat Transport (InSight) seismometer (Seismic Experiment for Interior Structure [SEIS]) has a strong daily quasi-periodicity and numerous transient microevents, associated mostly with an active Martian environment with wind bursts, pressure drops, in addition to thermally induced lander and instrument cracks. That noise is far from the Earth’s microseismic noise. Quantifying the importance of nonstochasticity and identifying these microevents is mandatory for improving continuous data quality and noise analysis techniques, including autocorrelation. Cataloging these events has so far been made with specific algorithms and operator’s visual inspection. We investigate here the continuous data with an unsupervised deep-learning approach built on a deep scattering network. This leads to the successful detection and clustering of these microevents as well as better determination of daily cycles associated with changes in the intensity and color of the background noise. We first provide a description of our approach, and then present the learned clusters followed by a study of their origin and associated physical phenomena. We show that the clustering is robust over several Martian days, showing distinct types of glitches that repeat at a rate of several tens per sol with stable time differences. We show that the clustering and detection efficiency for pressure drops and glitches is comparable to or better than manual or targeted detection techniques proposed to date, noticeably with an unsupervised approach. Finally, we discuss the origin of other clusters found, especially glitch sequences with stable time offsets that might generate artifacts in autocorrelation analyses. We conclude with presenting the potential of unsupervised learning for long-term space mission operations, in particular, for geophysical and environmental observatories.

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