Machine learning and marsquakes: a tool to predict atmospheric-seismic noise for the NASA InSight mission

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dc.contributor.authorStott, A. E.en
dc.contributor.authorGarcia, R. F.en
dc.contributor.authorChedozeau, A.en
dc.contributor.authorSpiga, A.en
dc.contributor.authorMurdoch, N.en
dc.contributor.authorPinot, B.en
dc.contributor.authorMimoun, D.en
dc.contributor.authorCharalambous, C.en
dc.contributor.authorHorleston, A.en
dc.contributor.authorKing, Scott D.en
dc.contributor.authorKawamura, T.en
dc.contributor.authorDahmen, N.en
dc.contributor.authorBarkaoui, S.en
dc.contributor.authorLognonne, P.en
dc.contributor.authorBanerdt, W. B.en
dc.date.accessioned2024-01-17T17:59:04Zen
dc.date.available2024-01-17T17:59:04Zen
dc.date.issued2023-01-04en
dc.description.abstractThe SEIS (seismic experiment for the interior structure of Mars) experiment on the NASA InSight mission has catalogued hundreds of marsquakes so far. However, the detectability of these events is controlled by the weather which generates noise on the seismometer. This affects the catalogue on both diurnal and seasonal scales. We propose to use machine learning methods to fit the wind, pressure and temperature data to the seismic energy recorded in the 0.4–1 and 2.2–2.6 Hz bandwidths to examine low- (LF) and high-frequency (HF) seismic event categories respectively. We implement Gaussian process regression and neural network models for this task. This approach provides the relationship between the atmospheric state and seismic energy. The obtained seismic energy estimate is used to calculate signal-to-noise ratios (SNR) of marsquakes for multiple bandwidths. We can then demonstrate the presence of LF energy above the noise level during several events predominantly categorized as HF, suggesting a continuum in event spectra distribution across the marsquake types. We introduce an algorithm to detect marsquakes based on the subtraction of the predicted noise from the observed data. This algorithm finds 39 previously undetected marsquakes, with another 40 possible candidates. Furthermore, an analysis of the detection algorithm’s variable threshold provides an empirical estimate of marsquake detectivity. This suggests that events producing the largest signal on the seismometer would be seen almost all the time, the median size signal event 45–50 per cent of the time and smallest signal events 5−20 per cent of the time.en
dc.description.versionSubmitted versionen
dc.format.extentPages 978-998en
dc.format.extent21 page(s)en
dc.identifier.doihttps://doi.org/10.1093/gji/ggac464en
dc.identifier.eissn1365-246Xen
dc.identifier.issn0956-540Xen
dc.identifier.issue2en
dc.identifier.orcidKing, Scott [0000-0002-9564-5164]en
dc.identifier.urihttps://hdl.handle.net/10919/117382en
dc.identifier.volume233en
dc.publisherOxford University Pressen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectPlanetary interiorsen
dc.subjectSeismic noiseen
dc.subjectPlanetary seismologyen
dc.titleMachine learning and marsquakes: a tool to predict atmospheric-seismic noise for the NASA InSight missionen
dc.title.serialGeophysical Journal Internationalen
dc.typeArticleen
dc.type.otherArticleen
dc.type.otherJournalen
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Scienceen
pubs.organisational-group/Virginia Tech/Science/Geosciencesen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Science/COS T&R Facultyen

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