Identification of new Marsquake clusters from P-wave coda analysis and implications for crustal structure and seismicity

Over the 1440-sol mission of the NASA InSight lander, more than 1900 seismic events (marsquakes) were recorded and catalogued by the Marsquake service [1]. These events have enabled the determination of both the bulk interior structure [e.g. 2,3,4] and hinted at more curious aspects, for example, a basal mantle layer [5,6], mantle plumes/dynamics [e.g. 7] and crustal heterogeneities [e.g. 8]. Much evidence for such features has come from a small select group of events, owing to their superior signal quality or magnitude. On top of this, only a handful of events have generated deep mantle phases [9].

Given that InSight is only a single station, it is necessary to develop methods that extract information from a broader range of marsquakes to further develop our understanding of event origins and interior structure. To this end, we propose a clustering approach for the P-wave coda of events. This method targets the relatively underused high frequency energy of events, typically dominated by scattering [8] and more affected by environmental noise [10]. The clustering approach effectively acts as a selective event stacking to improve signal to noise ratios, identifying only the most similar events to create the stack. Using this method, we identify a set of P-wave coda features and track their evolution over distances. 

We will explore the obtained families of P-waves morphology to infer the potential range of event sources and help improve marsquake classifications [1]. This is of interest to probe questions on whether they are generated from tectonic or impact sources [1], with recent research proposing the possibility of a swarm source for certain events [11]. This interpretation, in turn, has implications for the variability of Mars’s crustal structure. Furthermore, the signal to noise ratio improvement highlights delayed energy arrivals, providing new characteristics to infer structural complexities from scattering behaviour [8]. 

 

References

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[10] Stott, Alexander E., et al. "Machine learning and marsquakes: a tool to predict atmospheric-seismic noise for the NASA InSight mission." Geophysical Journal International 233.2 (2023): 978-998.
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