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Identifying deep moonquake nests using machine learning model on single lunar station on the far side of the Moon
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  • Josipa Majstorović,
  • Philippe Lognonné,
  • Taichi Kawamura,
  • Mark Paul Panning
Josipa Majstorović
Institut de Physique du Globe de Paris

Corresponding Author:[email protected]

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Philippe Lognonné
Université Paris Cité, Institute de physique de globe de Paris, CNRS
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Taichi Kawamura
Université Paris Cité, Institut de physique du globe de Paris, CNRS
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Mark Paul Panning
Jet Propulsion Laboratory, California Institute of Technology
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One of the future NASA space program includes the Farside Seismic Suite (FSS) payload, a single station with two seismometers, on the far side of the Moon. During FSS operations, the processing of the data will provide us with new insight into the Moon’s seismic activity. One of Apollo mission finding is the existence of deep moonquakes (DMQ), and the nature of their temporal occurrence patterns as well as the spatially clustering. It has been shown that DMQs reside in about 300 source regions. In this paper we tackle how we can associate new events with these source regions using the single station data. We propose a machine learning model that is trained to differentiate between DMQ nests using only the lunar orbital parameters related to DMQ time occurrences. We show that ML models perform well (with an accuracy >70%) when they are trained to classify less than 4 nests.
09 Aug 2023Submitted to ESS Open Archive
12 Aug 2023Published in ESS Open Archive