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Investigation of Mars seismic noise using modelling techniques developed on Earth
Published in Elsevier B.V.
Volume: 53
Issue: 1
Pages: 628 - 633
Systems and signals theory has aided in understanding various kinds of processes such as meteorology, econometrics, engineering, etc. In this work, we explore the applicability of these theories to analyze phenomenon from other terrestrial bodies, specifically the seismic phenomenon on Mars, using data-driven approaches. It is expected that this analysis will provide insights into the formation and interior of the red planet. It will also allow us to draw a preliminary comparison of the seismic phenomenon between the home and red planet. The specific objectives of this work are (i) exploratory analysis of the statistical characteristics and (ii) develop a time-series model using a systematic procedure that was initially developed to analyze Earth data. It involves a rigorous investigation of the specific statistical properties, namely, stationarity, linearity, and Gaussianity, followed by the development of a suitable time-series model that is commensurate with these properties. Our analysis reveals that Mars noise exhibits specific types of non-stationarities, namely, trend and heteroskedasticity. In addition, noise also exhibits linearity and follows a Gaussian distribution. In line with these features, we develop a component model that comprises of a third-order polynomial trend and the Autoregressive Integrated Moving Average - Generalized AR Conditional Heteroskedasticity (ARIMA-GARCH) model. The findings reveal that the specific properties share a striking similarity with home planet data, while the trend appears to be a unique feature. These discoveries, we believe, will pave the way in understanding the red planet. The studies, including model development, are carried out on datasets recorded by the seismometer deployed on Mars during NASA's Insight mission. © 2020, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
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PublisherData powered by TypesetElsevier B.V.
Open AccessNo