The Marsquake Service: Securing Daily Analysis of SEIS Data and Building the Martian Seismicity Catalogue for InSight

Atmospheric Science with InSight

“…daytime turbulent convection, or an episode of very windy nighttime conditions. This is also why the Mars Quake Service (Clinton et al 2018) will be complemented by a Mars Weather Service analyzing the APSS observations in a broader sense than the approach driven by seismic events. To further reach this goal to monitor NEMOs, opacity measurements will be carried out with cameras and the use of InSight's arm, as is described in Sect.…”

Geology and Physical Properties Investigations by the InSight Lander

“…At low frequencies, ground tilt is expected to vary due to a number of sources, such as the thermal tilt of the subsurface (see Clinton et al 2017Clinton et al , 2018 for time domain simulations), static loading of the lander in response to wind dynamic pressure (Murdoch et al 2017a), and static loading caused by atmospheric pressure fluctuations due to planetary boundary layer activity (Murdoch et al 2017b), including those associated with dust devils . All of these sources generate significant seismic noise , that can also be treated as signal and processed together with the pressure data to provide a profile of the subsurface shear modulus (Sect.…”

Preparing for InSight: Evaluation of the Blind Test for Martian Seismicity

“…Travel-time curves for the most prominent phases are shown in the legend. The waveforms are band-pass filtered between 1.5 and 10 s. Stähler and Sigloch (2014) has been applied successfully after the submission deadline by the MQS team for the largest three events (Clinton et al, 2018).…”

“…Detection: visual (data and spectrograms) and automated event detection (STA/LTA triggers with variable parameter settings, spectrogram detector) Location: visual azimuth determination using hodograms; distance based on relative P, S, R1, and multiple orbit surface waves Other efforts: correct model chosen based on travel times and dispersion curves; automated pressure event classification Houston Location: Surface-wave polarization for azimuth (Vidale, 1986); relative surface-wave travel times for distance (including minor arc only) Other efforts: high-resolution dispersion analysis of multiorbit surface waves to determine phase velocity and the correct model (Zheng et al, 2015;Zheng and Hu, 2017); depth based on depth phases IPGP Key efforts: autocorrelation to detect crustal discontinuities (Schimmel, 1999;; degree of polarization Rayleigh-wave detection and azimuth ; no catalog submitted Max Planck Key efforts: automated event detection and classification using HMMs (Hammer et al, 2012(Hammer et al, , 2013Knapmeyer-Endrun and Hammer, 2015); no catalog submitted Marsquake service Detection: event detection by visual screening of spectrograms Location: four probabilistic methods for distance and azimuth for body-and surface waves (Böse et al, 2016); new model set for probabilistic methods based on the largest events; distances refined by visual alignment of waveforms vs. distance for all events; multiple iterations in relocation to detect outliers Magnitudes: Böse et al (2018) Other efforts: event classification based on quality of location (Clinton et al, 2018); correct model chosen; by comparing event waveforms at similar distances, depths were indicated and one event was correctly identified as an impact Oxford Detection: visual event detection on band-pass filtered traces Location: differential travel times and surface-wave dispersion for distance; particle motion and polarization for azimuth (three different methods); detailed description in Fernando et al (2018) Other efforts: three models suggested, including the correct one Utah Detection: manual event detection assisted by STA/LTA using multiple filter bands and polarization (Jurkevics, 1988;Allam et al, 2014;Ross and Ben-Zion, 2014) Location: azimuth based on P and Rayleigh polarization; distance based on relative Pand S travel times Other efforts: model wrongly detected based on H/V ratio (Lin et al, 2014) and receiver functions (Allam et al, 2017); event classification based on radial-to-transverse ratio H/V, horizontal-to-vertical; HMM, Hidden Markov model; IPGP, Institut de Physique du Globe de Paris; STA/LTA, short-term average/long-term average.…”