TP12

After an auspicious beginning, with the Ranger, Apollo and Viking missions carrying multiple seismometers into space, planetary seismology lay dormant for 40 years, save for a cruelly-extinguished hope on the Mars’96 mission. InSight put an end to this seismological drought with its landing on Mars in November, 2018, and subsequent deployment of the SEIS instrument two months later (Banerdt et al., 2020; Lognonné et al., 2019, 2020).

After a slow start (uncannily, no seismic events were detected for the first two months after deployment), the subsequent three-plus years  have been an unqualified triumph for planetary seismology. Roughly 2700 seismic events have been detected and cataloged (Clinton et al., 2021; Ceylan et al., 2022), ranging from more than a thousand tiny SF events, thought to be locally-sourced thermal events, to a massive magnitude 5 marsquake more than 2000 km away, but still with a signal-to-noise ratio over 160,000. This catalog includes more than 30 Low Frequency and Broad-Band marsquake signals of quality A or B, meaning they have clear body-wave phase arrivals and, in the case of quality A, identifiable polarization. These have been clearly identified as teleseismic events, which taken together describe a set of ray paths that have probed the interior of the planet from the surface down to its core.

The analysis of this seismic data set has already transformed our knowledge of the interior structure of Mars. We have for the first time been able to directly measure the thickness of the crust beneath InSight (Knapmeyer-Endrun, et al., 2021; Kim et al., 2021; Durán et al., 2022; Drilleau et al., 2022) and the size of the martian core (Stähler et al., 2021). Analysis of the data has further allowed us to place tight constraints on the density of the core and crust, and the thermal profile of the mantle (Stähler et al., 2021; Khan et al., 2021, 2022; Wieczorek et al., 2022). In addition, SEIS’s observations have proved invaluable for studying the near-surface structure beneath InSight on scales from tens of centimeters to hundreds of meters (e.g., Lognonné et al., 2020; Banerdt et al., 2020; Kenda et al., 2020; Hobiger et al., 2021; Murdoch et al., 2021; Compaire et al., 2021, 2022).

InSight’s seismometer has also been able to study the current tectonics of Mars, providing a measure of the size distribution of seismic events (and thus a window into the rate of moment release) and a map of their geographic distribution (in particular, the strong concentration of Eastern hemisphere activity around Cerberus Fossae; Giardini et al., 2020; Stähler et al., 2022) and even revealing the source mechanisms responsible for some marsquakes, from which the orientation of stresses in the lithosphere can be inferred (Brinkman et al., 2021).

InSight and SEIS have proven conclusively the value of planetary seismology in general, and a single high-quality seismometer in particular, in understanding the solid planets of our solar system. The results described in this talk are only a subset of the work that has been done, and was derived from just the first round of analysis of SEIS data. As the seismic community becomes more familiar with the data set and it becomes more widely used, we can expect new insights into the geophysics of Mars for decades to come.

References:

Banerdt, W. B., and Smrekar, S. E., et al., 2020. Initial results from the InSight mission on Mars. Nat. Geosci. 13, 183–189. https://doi.org/10.1038/s41561-020-0544-y

Brinkman, N., et al., 2021. First focal mechanisms of marsquakes, J. Geophys. Res. Planets, e2020JE006546. https://doi.org/10.1029/2020JE006546

Ceylan, S., et al., 2022. The marsquake catalogue from InSight, sols 0-1011, Phys. Earth Planet. Int., in press.

Clinton, J., et al., 2021. The Marsquake Catalogue from InSight, Sols 0-478, Phys. Earth Planet. Int. 310, 106597. https://doi.org/10.1016/j.pepi.2020.106597

Compaire, N., et al., 2021. Autocorrelation of the ground vibrations recorded by the SEIS-InSight seismometer on Mars, J. Geophys. Res. Planets 126, e2020JE006498. https://doi.org/10.1029/2020JE006498

Compaire, N., et al., 2022. Seasonal variations of subsurface seismic velocities monitored by the SEIS-InSight seismometer on Mars, Geophys. J. Int. 229:2, 776–799. https://doi.org/10.1093/gji/ggab499

Drilleau, M., et al., 2022. Marsquake location and 1-D seismic models for Mars from InSight data, J. Geophys. Res. Planets, in press.

Durán, C., et al., 2022. Seismology on Mars: An analysis of direct, reflected, and converted seismic body waves with implications for interior structure, Phys. Earth Planet. Int., in press.

Hobiger, M., et al., 2021. The shallow structure of Mars from inversion of high-frequency ambient seismic vibrations Rayleigh wave ellipticity at the InSight landing site, Nat. Comm. 12, 6756,. https://doi.org/10.1038/s41467-021-26957-7

Kenda, B., et al., 2020. Subsurface structure at the InSight landing site from compliance measurements by seismic and meteorological experiments, J. Geophys. Res. Planets 125, 6, e2020JE006387. https://doi.org/10.1029/2020JE006387

Khan, A., et al., 2021. Upper mantle structure of Mars from InSight seismic data, Science, 373, 434-438. https://doi.org/10.1126/science.abf2966

Khan, A., et al., 2022. Geophysical and cosmochemical constraints on the bulk composition of Mars , Earth Planet. Sci. Lett. 578. https://doi.org/10.1016/j.epsl.2021.117330

Kim, D., et al., 2021. Improving constraints on planetary interiors with PPs receiver functions, J. Geophys. Res. Planets 126, e2021JE006983. https://doi.org/10.1029/2021JE006983

Knapmeyer-Endrun, B., et al., 2021. Thickness and structure of the martian crust from InSight seismic data, Science, 373, 438-443. https://doi.org/10.1126/science.abf8966

Lognonné, P., et al., 2020. Constraints on the shallow elastic and anelastic structure of Mars from InSight seismic data. Nat. Geosci. 13, 213–220. https://doi.org/10.1038/s41561-020-0536-y

Murdoch, N., et al., 2021. Constraining Martian regolith and vortex parameters from combined seismic and meteorological measurements, J. Geophys. Res. Planets 126, e2020JE006410. https://doi.org/10.1029/2020JE006410

Stähler, S. C., et al., 2021. Seismic detection of the martian core, Science, 373, 443-448. https://doi.org/10.1126/science.abi7730

Stähler, S., et al., 2022. Marsquakes indicate dike-induced tectonics in Cerberus Fossae, Mars, Nature Geoscience, in press.

Wieczorek, M., et al., 2022. InSight constraints on the global character of the Martian crust, J. Geophys. Res. Planets, e2022JE007298. https://doi.org/10.1029/2022JE007298

How to cite: Banerdt, B. and the InSight Science Team: InSight’s Contributions to Planetary Seismology and Geophysics, Europlanet Science Congress 2022, Granada, Spain, 18–23 Sep 2022, EPSC2022-773, https://doi.org/10.5194/epsc2022-773, 2022.

We report the results of more than 2 years of monitoring the rotation of Mars with the RISE instrument on InSight. Small periodic variations of the spin axis orientation, called nutations, can be extracted from the Doppler data with enough precision to identify the influence of the Martian fluid core. For the first time for a planetary body other than the Earth, we can measure the period of the Free Core Nutation (FCN), which is a rotational normal mode arising from the misalignment of the rotation axes of the core and mantle. In this way, we confirm the liquid state of the core and estimate its moment of inertia as well as its size. 

The FCN period depends on the dynamical flattening of the core and on its ability to deform. Since the shape and gravity field of Mars deviate significantly from those of a uniformly rotating fluid body, deviations from that state can also be expected for the core. Models accounting for the dynamical shape of Mars can thus be tested by comparing core shape predictions to nutation constraints. The observed FCN period can be accounted for by interior models having a very thick lithosphere loaded by degree-two mass anomalies at the bottom. 

The combination of nutation data and interior structure modeling allows us to deduce the radius of the core and to constrain its density, and thus, to address the nature and abundance of light elements alloyed to iron. The inferred core radius agrees with previous estimates based on geodesy and seismic data. The large fraction of light elements required to match the core density implies that its liquidus is significantly lower than the expected core temperature, making the presence of an inner core highly unlikely. Besides, the existence of an inner core would lead to an additional rotational normal mode the signature of which has not been detected in the RISE data. 

How to cite: Rivoldini, A., Le Maistre, S., Caldiero, A., Yseboodt, M., Baland, R.-M., Beuthe, M., Van Hoolst, T., Dehant, V., Folkner, W., Buccino, D., Kahan, D., Marty, J.-C., Antonangeli, D., Badro, J., Drilleau, M., Konopliv, A., Peters, M.-J., Plesa, A.-C., Samuel, H., and Tosi, N. and the InSight/RISE team: A view into the deep interior of Mars from nutation measured by InSight RISE, Europlanet Science Congress 2022, Granada, Spain, 18–23 Sep 2022, EPSC2022-1101, https://doi.org/10.5194/epsc2022-1101, 2022.

Introduction: NASA’s InSight lander has been operating on the surface of Mars for over 1200 Martian solar days (sols). Despite decreasing power, the lander has acquired near continuous seismic data throughout the mission to date. The Marsquake Service (MQS) performs daily analysis of the data and catalogues the seismicity. The catalogue currently contains over 1300 local, regional and teleseismic marsquakes and more than 1300 events that are likely associated with thermal cracking close to the lander (Table 1). Here we will present the latest update on the marsquake catalogue.

Martian Seismic Data: The Martian seismic dataset is often heavily contaminated by atmospheric effects, especially during the daytime and in the Northern hemisphere autumn and winter (Figure 1)[1,2]. This environmental noise can obscure the seismicity which is often at low amplitudes. During spring and summer, the atmospheric conditions are much more favourable from shortly before sunset until approximately sunrise and much of the MQS catalogue is detected during these hours. Other transient effects on the data that can interfere with event detection are discussed in [3]. One notable feature of the seismic noise on Mars is a resonance at 2.4 Hz which is observed during quiet periods and is also excited by seismic events that contain energy at that frequency. This resonance is likely caused by sub-surface structure [4] and greatly enhances the detection rate of higher frequency events.

Figure 1: Daily spectrograms of VBB vertical component stacked from sol 72 to sol 1233 with catalogued marsquakes overlain. White lines are where data were not recorded. The black dotted line represents the equivalent sol in mission year 1 to sol 1233. The black dashed line marks 1 year since SEIS was fully deployed.

Marsquake Types and Qualities: MQS categorises marsquakes based on their frequency content and their location quality [2,5]. A full description of the categories and qualities can be found in [2], here we list the general characteristics:

Low Frequency (LF): All energy is below 2.4 Hz and most of the time below 1 Hz.

Broadband (BB): Energy is predominantly at low frequencies but can include energy at and above 2.4 Hz.

2.4 Hz: Energy is contained in a narrow frequency band around 2.4 Hz, reaching a maximum of 4 Hz. These events are thought to be low amplitude versions of High Frequency events.

High Frequency (HF): Energy is predominantly at 2.4 Hz but also extends above 4 Hz. There may also be energy at low frequencies but the dominant energy is at higher frequencies (as opposed to BB events).

Very High Frequency (VF): Energy is at high frequencies, sometimes reaching up to 30 Hz. Energy is strongest on the horizontal components, especially at higher frequencies.

Super High Frequency (SF): very short duration (~10s of seconds) events with mainly horizontal energy between 5-10 Hz. These events are highly temperature dependent and are likely due to thermal cracks observed in the near surface close to the lander [6].

For all except SF events a location quality is assigned based on the ability to identify phase picks and locate the event as follows:

Quality A: Multiple clear phases are identified, and clear polarisation can be defined. The source is well located with distance and backazimuth.

Quality B: Either the distance or the backazimuth is clear but not both. A full location cannot be provided. When clear phases are identified distance is calculated.

Quality C: Clear seismic signal is observed but phases are not readily assigned, nor polarisation identified. A distance may be assigned but will be uncertain.

Quality D: The signal has low signal to noise either due to a low amplitude signal or high contamination from environmental noise. No location is given and there is a small risk that the event may not be tectonic in origin.

The Marsquake Catalogue: The marsquake catalogue is released every three months with a three-month delay. The latest release is V10 [7]. The catalogue includes not only the type and quality of the events but also general information such as location, origin, magnitude and any secondary phases that have been picked. Table 1 shows the total seismicity catalogued up to sol 1233.

Table 1

Noteworthy New Events: The V10 catalogue [7] and data release [8] contain some significant new events including 6 new Quality A events. Most notable is S1094b, BB, QA. This event, with M_w^Ma=4.0 is one of the largest BB events ever detected and originates in a region where no previous seismicity had been seen with a distance of ~60° and a backazimuth of 40°.

We will also introduce the largest event ever recorded on Mars, S1222a.

Summary: The InSight lander has provided an incredible seismic dataset and the MQS has meticulously catalogued the seismicity. Now well into its extended mission the seismicity from the second Martian year of operations has been exceptional and MQS will continue to add to the catalogue.

References: [1] Lognonné et al., 2020, Nature Geo., 13(3), 213-220. DOI: 10.1038/s41561-020-0536-y, [2] Clinton et al., 2021, Physics of the Earth and Planetary Interiors, 310, 106595, DOI: 10.1016/j.pepi.2020.106595, [3] Ceylan et al., 2021, Physics of the Earth and Planetary Interiors, 310, 106597, DOI: 10.1016/j.pepi.2020.106597, [4] Hobiger et al., 2021, Nature Comms, 12(1), 1-13, DOI: 10.1038/s41467-021-26957-7, [5] Giardini et al., 2020, Nature Geo., 13(3) 205-212, DOI: 10.1038/s41561-020-0539-8, [6] Dahmen et al., 2020, J. Geophys. Res., 126, p.e.2020JE006599, DOI: 10.1029/2020JE006599, [7] InSight Marsquake Service (2022), Mars Seismic Catalogue, InSight Mission; V10 2022-04-01, ETHZ, IPGP, JPL, ICL, Univ. Bristol,
https://doi.org/10.12686/a16, [8] InSight Mars SEIS Data Service, 2022, SEIS raw data, InSight Mission, IPGP, JPL, CNES, ETHZ, ICL, MPS, ISAE-Supaero, LPG, MFSC, DOI: 10.18715/SEIS.INSIGHT.XB_2016

How to cite: Horleston, A., Clinton, J., Ceylan, S., Kawamura, T., Stähler, S. C., Charalambous, C., Dahmen, N. L., Duran, C., Kim, D., Plasman, M., Zenhäusern, G., Euchner, F., Knapmeyer, M., Giardini, D., Lognonné, P., Pike, W. T., Panning, M., Smrekar, S., and Banerdt, W. B.: The Seismicity of Mars as Recorded by InSight's Marsquake Service, Europlanet Science Congress 2022, Granada, Spain, 18–23 Sep 2022, EPSC2022-880, https://doi.org/10.5194/epsc2022-880, 2022.

Abstract

The seismic data collected by the SEIS (seismic experiment for the interior structure of Mars) [1] package on the NASA InSight mission provide a way to obtain the structure and properties of Mars [2,3]. As the seismometer is installed on the surface, it is also sensitive to atmospherically generated signals from the pressure and wind [4]. These can constrain the ability to observe seismic events. To that end, in Stott et al. 2022 [5], we recently proposed machine learning models to predict the seismic energy from the pressure, wind and temperature data recorded by the dedicated sensors in the APSS (auxiliary payload sensor suite) and TWINS (temperature and wind for InSight) sensor packages. This yields a tool to analyse and discover marsquakes.

A machine learning model for predicting seismic noise

The inputs used for the machine learning model are:

• The pressure and the envelope of the pressure
• The wind speed and direction from two sensor booms
• The air temperature from each sensor boom

These inputs are fed into the machine learning models to predict the seismic velocity energy for the vertical (Z) and horizontal (North and East) axes in the low frequency (0.4-1Hz) and high frequency (2.2-2.6Hz) regions, as these cover the predominant excitations in both categories of marsquakes in the catalogue [6].

Two methods were proposed, the first using gaussian process regression [7] and the second using a neural network [8]. The neural network was found to have superior prediction performance. However, the gaussian process regression model could be used to better dig into the physical relationships between wind, pressure and the ground motion owing to the form and the probabilistic nature of the method.

Marsquake analysis

Figure 1 shows the prediction and observed data for a period containing a marsquake. Before and after the marsquake the observed and predicted data match well, but during the marsquake they diverge indicating the event not to be generated by atmospheric effects. This event is classified as a 2.4Hz event. This class of event typically only exhibits excitation around the 2.4Hz resonance [6,9,10]. However, the prediction in the 0.4-1Hz bandwidth indicates there is some separate energy. Examining the machine learning prediction across the marsquake catalogue shows low frequency energy occurs consistently in the higher frequency classes of events for greater event magnitudes.

Detecting new events and seismic detectivity

The proposed machine learning models identify marsquakes as separate from the background atmospherically generated noise. This can be used to discover new marsquakes as outliers. An algorithm based on this concept was proposed and is demonstrated in Figure 2. Using this approach, a further 32 good candidate events are found along with another 34 suggestions.

Conclusion

Machine learning is a nascent technology in planetary science, whereby it provides a powerful tool to fit datasets using mathematical complexity. Here, we present a model to fit the atmospheric data to the seismic data from the NASA InSight mission. In doing so we

• Provide insights on the relationship between ground motion and the wind, pressure and temperature
• Provide a tool to analyse marsquakes
• Enable the detection of new marsquakes

In this way the machine learning models offer new information about marsquakes and also enable the study of their detectivity to help place constraints on the seismicity of Mars.

References

[1] Lognonné et al. SSR 2019

[2] Giardini et al. Nat. Geosci 2020

[3] Stähler et al. Science 2022

[4] Charalambous et al. JGR: Planets 2021

[5] Stott et al. Submitted 2022

[6] Clinton et al. PEPI 2021

[7] Williams and Rasmussen, MIT Press, 2006

[8] Goodfellow et al. MIT Press 2016

[9] Compaire et al. JGR: Planets 2021

[10] Dahmen et al. BSSA 2021

Figure 1: The S0534a 2.4Hz event with the machine learning model prediction. The top panel shows the observed seismic energy compared to the prediction where the energy during the event is seen to diverge. The second and third panels show the SNR (signal to noise ratio) calculated between the observed and predicted signal for the HF (2.2-2.6Hz) and LF (0.4-1Hz) bandwidths respectively. The bottom panel shows the velocity spectrogram during the event.

Figure 2: Figure showing the detection of a new event with the algorithm in [5]. The top panel shows the prediction from the machine learning model and the observed data. The middle panel shows the detrended residual and the smoothed MAE (mean absolute error) highlighting the outlying points indicating an event. The bottom panel shows the histogram (over a 2 hour window) of the MAE where the points belonging to the event are seen to be outliers in the distribution.

How to cite: Stott, A., Garcia, R., Chédozeau, A., Spiga, A., Murdoch, N., Pinot, B., Mimoun, D., Charalambous, C., Horleston, A., King, S., Kawamura, T., Dahmen, N., Barkaoui, S., Lognonné, P., and Banerdt, W.: Using machine learning to separate atmospherically generated noise from marsquakes, Europlanet Science Congress 2022, Granada, Spain, 18–23 Sep 2022, EPSC2022-833, https://doi.org/10.5194/epsc2022-833, 2022.

The InSight (Interior Exploration using Seismic Investigations, Geodesy and Heat Transport) lander is providing an unprecedented set of high frequency records of ground deformations, pressure and wind at the surface of Mars. Seismic and acoustic waves from impacts have been observed by SEIS seismometer through the ground movements that they generate, and their sources as impacts have been confirmed with orbital images of newly appearing craters. Three impacts have been identified and located, and one additional seismic event is consistent with a date-constrained new impact. Several other events are currently being investigated as well. Arrival times and polarization of seismic and acoustic waves were used to estimate impact locations. These estimated locations were subsequently confirmed by orbital imaging of associated craters and temporal matches to the events times, with previous orbital images showing no craters. Crater dimensions and estimates of meteoroid trajectories from images allow us to understand and model the recorded seismograms. The precise source locations provided by impacts as compared to tectonic sources provide direct constraints on the structure of the martian interior. First arrival seismic waves confirm the previously determined crustal models, and the dispersion of trapped acoustic waves confirms the current models of sound speed and wind in the atmosphere. In addition, these observations provide the first ground-truth for distance-amplitude scaling relationships between impacts and the mechanical waves for Mars. They confirm the relationship between the seismic moment of impacts and the vertical impactor momentum, and demonstrate the capability of planetary seismology to constrain impact rates and internal structure of terrestrial planetary objects throughout the solar system.

How to cite: Garcia, R. F. and the InSight and CTX science teams: First seismo-acoustic location and orbital imaging of recent impacts on Mars: constraints on atmosphere and interior structure and impact processes, Europlanet Science Congress 2022, Granada, Spain, 18–23 Sep 2022, EPSC2022-10, https://doi.org/10.5194/epsc2022-10, 2022.

The InSight (Interior Exploration using Seismic Investigations, Geodesy and Heat Transport) mission has been collecting high-quality seismic data on Mars for over 1200 Martian solar days (sols), providing the first direct observations of its interior structure. In this study, we report on a complete analysis of low-frequency (LF) seismic evens (main energy below 1 Hz), that have proven essential for understanding the deep interior of Mars [1], [3]-[5]. As Martian seismograms are generally characterized by a low signal-to-noise ratio and strong scattering, data selection and processing is central for the identification and consistent picking of body-wave arrivals. To this end, we employ complementary approaches, including: 1) narrow-band-filtered time-domain envelopes; 2) polarised waveforms and their time-domain envelopes; 3) polarisation analysis; and 4) waveform matching. An example of the application of the first two approaches to identify body-wave arrivals is shown in Fig 1. The careful application of this 4-step processing scheme to each LF event has resulted in a significant increase in body wave phase picks, encompassing direct, reflected, and converted body waves, relative to earlier analyses [1]. With our method, all phases are obtained through iterative refinement, which relies on inverting an initial set of phase picks for models of interior structure, event distance, and depth, while predicting travel times for additional seismic phases. Based on the predictions, we re-pick (every pick is subject to our 4-step processing scheme), thereby enlarging our differential travel-time data set (all picks are relative to the main P- or PP-wave arrival), and subsequently re-invert for an updated set of interior structure models, event distances, and depths. Through application of iterative refinement to new seismic data, we continuously update information on radial velocity models of the crust, mantle, and core, as well as source parameters, and phase picks. The recent identification of the most distant marsquakes recorded by the InSight mission and the first core-diffracted P-wave [2] allows constraints on the P-wave velocity structure of the lower mantle (see distant event in Fig 2) that hitherto could not be constrained because of lack of lower-mantle-traversing P-waves. In addition, on sol 1222, InSight detected the largest event ever recorded on Mars, providing a unique opportunity to understand the interior of Mars through the identification of waves that could not be identified in earlier events because of their low signal-to-noise ratio. Our iterative refinement procedure demonstrates that through continued analysis of new events, our knowledge and understanding of the interior of Mars will steadily improve.

Figure 1: Vertical-component time-domain envelopes and polarised traces for event S0235b across different frequency bands (filter banks). Grey envelopes depict unpolarised time-domain envelopes. Polarised traces and their envelopes are shown in black (lines) and colored envelopes, respectively. Initial seismic phase picks for S0235b and their uncertainties are shown as vertical dotted lines and orange bars, respectively. . The vertical grey bar in panel (A) represents a glitch. Panel (B) shows a zoomed-in version of the P-wave phases in (A). The vertical dashed line in (B) denoted by X, indicates another possible seismic wave arrival. Amplitudes of traces and envelopes are normalized by their maxima and scaled for better visualisation.

Figure 2: Body-wave (P, S, PP, SS, PPP, SSS, ScS and Pdiff) ray path geometry for 15 low-frequency events. Events are indicated by green stars and InSight is represented by a black triangle. The colorbar indicates ray path density in the form of the number of rays that pass through a certain area based on inverted models [1], which accounts for the diffuseness of ray paths. The radial sensitivity computed as the integrated ray path density with epicentral distance is shown at the bottom.

References:

[1] C. Durán, A. Khan, S. Ceylan, et al., “Seismology on Mars: An analysis of direct, reflected, and converted seismic body waves with implications for interior structure,” Physics of the Earth and Planetary Interiors, vol. 325, p. 106 851, 2022, issn: 0031-9201. doi: 10.1016/j.pepi.2022.106851.

[2] A. C. Horleston, J. F. Clinton, S. Ceylan, et al., “The Far Side of Mars: Two Distant Marsquakes Detected by InSight,” The Seismic Record, vol. 2, no. 2, pp. 88–99, Apr. 2022, issn: 2694-4006. doi: 10.1785/0320220007.

[3] A. Khan, S. Ceylan, M. van Driel, et al., “Upper mantle structure of Mars from InSight seismic data,” Science, vol. 373, no. 6553, pp. 434–438, 2021, issn: 0036-8075. doi: 10.1126/science.abf2966.

[4] A. Khan, P. Sossi, C. Liebske, A. Rivoldini, and D. Giardini, “Geophysical and cosmochemical evidence for a volatile-rich Mars,” Earth and Planetary Science Letters, vol. 578, p. 117 330, 2022, issn: 0012-821X. doi: 10.1016/ j.epsl.2021.117330.

[5] S. C. Stähler, A. Khan, W. B. Banerdt, et al., “Seismic detection of the martian core,” Science, vol. 373, no. 6553, pp. 443–448, 2021, issn: 0036-8075. doi: 10.1126/science.abi7730.

How to cite: Durán, C., Khan, A., Ceylan, S., and Giardini, D.: Seismology on Mars: Analysis and identification of body-waves with implications for Martian structure, Europlanet Science Congress 2022, Granada, Spain, 18–23 Sep 2022, EPSC2022-969, https://doi.org/10.5194/epsc2022-969, 2022.

How to cite: Samuel, H., Drilleau, M., Garcia, R., Huang, Q., Rivoldini, A., Lognonné, P., and Banerdt, B.: Testing the Presence Deep Martian Mantle Layering in the light of InSight Seismic Data, Europlanet Science Congress 2022, Granada, Spain, 18–23 Sep 2022, EPSC2022-297, https://doi.org/10.5194/epsc2022-297, 2022.

On the 1222nd sol of the InSight mission (or May the Fourth of 2022), a seismic event was detected that turned out to be the largest marsquake recorded so far. At a moment magnitude of 4.7, event S1222a released as much seismic moment as all seismic events previously catalogued by InSight together, and greatly surpasses the second largest event, S0976a.

The frequency of occurrence of earthquakes follows a power law with an exponent (the b value) close to 1 over a wide range of magnitudes. The class of Low Frequency marsquakes, to which S1222a belongs, shows a similar behaviour. At low magnitudes, the slope of the cumulative distribution suggests that the InSight marsquake catalog for Low Frequency events is representative for events with moment magnitude exceeding 3 (Figure 1). Up to and including the occurrence of event S0976a one could however guess that events larger than magnitude approx. 3.6 might be less frequent than predicted by this power law. Event S1222a mends this apparent decrease, and the b value derived from all events is 1 within the formal uncertainty. Hence the resulting distribution follows a power law for events exceeding magnitude 3.

Figure 1 Size frequency distribution of Low Frequency and Broadband events. Red: status before occurrence of S1222a, blue: afterwards. The dashed lines represent the Maximum Likelihood solutions for fitting a Power Law distribution, with b-values as indicated in the legend.

Since S0976a occurred at an epicentral distance of about 140° (Horleston et al., 2022) at night time(02:26 LMST), but was recorded with amplitudes high enough to be visible during typical day time noise, we can infer that the catalog is complete for events the size of S0976a or larger. Since the slope of the distribution appears to be stable over the entire magnitude range from 3 up to that of S1222a, we conclude that the catalogue of Low Frequency events is not only representative, but complete for events larger than 3.

The magnitude of S1222a is slightly to the right of the value predicted by the power law, but, compared to other deviations, not excessively so. Also, the distribution does not show an indication of increasing slope any more. With about 30 events of magnitude 3 during the 3.3 years of InSight observations so far, this suggests that events larger than S1222a can be expected with respective recurrence rates: A magnitude 5 event appears likely about once in 11 years, i.e. on a decadal scale.

We use the approach of Knapmeyer et al. (2018) to estimate the seismic moment rate of Mars, and also the corner magnitude above which the size-frequency distribution becomes much steeper and events larger than the corner magnitude extremely unlikely (our analysis uses the Tapered Gutenberg-Richter distribution with an exponential roll-off above the corner frequency). The simple idea of Knapmeyer et al. (2018) was that, since a few large events release most of the seismic moment, the moment of the largest event ever observed, scaled with the duration covered by the catalog and a factor depending on b value and corner moment, provides a good estimation of the moment rate. Including not only the one largest, but the n largest events (with n between 1 and 10 or so) provides an even better estimation. In the 2018 study we have shown that the approach yields a surprisingly good estimation of the Earth's moment rate after a few months of registration, rather than after the many decades necessary to witness a magnitude 9 event.

We employ here an estimation based on the 5 largest events in the catalog. Knapmeyer et al. (2018) have shown that this low number may already provide a reasonable rate estimate. At the same time we attempt to avoid using events too small to be globally detectable. Future analyses will show if the use of 5 events is actually the best choice. The inclusion of event S1222a shifts the estimated moment rate slightly upwards, as well as the estimated corner moment. Figure 2 shows that both parameters are slightly below those of the WeakMany model of Knapmeyer et al., (2006). Compared to the catalog from before its occurrence, the parameter modifications due to S1222a are hardly significant (and not shown here). At least as important is the reduction of the uncertainty region in the moment-rate / corner-moment parameter space. We cannot yet exclude that the moment rate of Mars is as low as that of the Moon, but if it were, it would be rather unlikely to observe the event sequence that InSight observed.

Figure 2 Estimation of Moment Rate and Corner Moment. For each Pixel, 10000 synthetic catalogs were evaluated to compare their parameters with the five largest events from the InSight catalog (KS₅ estimation). Colour indicates the probability that the five largest events drawn from a tapered Gutenberg Richter distribution in 3.3 years reproduce the KS5 estimation obtained from the observed events, to within an 80 %  interdecile around the median. The vertical line indicates the moment rate for the Moon (observed, from Shallow Moonquakes), markers indicate the WeakMany and Medium models of Knapmeyer et al. (2006). The moment rate of the Earth corresponds to an equivalent magnitude of about 8.5 (Knapmeyer et al., 2018). Horizontal lines indicate the magnitude of the largest event and its uncertainty. The maximum of the distribution is marked by a small white circle.

References

Horleston, A. C., et al. (2022). The Far Side of Mars: Two Distant Marsquakes Detected by InSight, The Seismic Record. 2(2), 88–99, doi: 10.1785/0320220007

Knapmeyer, M., Oberst, J., Hauber, E., Wählisch, M., Deuchler, C., Wagner, R. (2006). Working models for spatial distribution and level of Mars' seismicity. Journal of Geophysical Research, vol. 111, E11006, doi:10.1029/2006JE002708

Knapmeyer, M., et al. (2018). Estimation of the Seismic Moment Rate from an Incomplete Seismicity Catalog, in the Context of the InSight Mission to Mars, Bull. Seis. Soc. Am., vol. 109, No. 3, 1125-1147, doi: 10.1785/0120180258

Taylor et al., (2013). Estimates of seismic activity on the Cerberus Fossae region of Mars, Journal of Geophysical Research, vol. 118, 2570-2581, doi:10.1002/2013JE004469

How to cite: Knapmeyer, M., Ceylan, S., Charalambous, C., Clinton, J., Dahmen, N., Duran, C., Horleston, A., Kawamura, T., Kim, D., Plasman, M., Stähler, S., Zenhäusern, G., Weber, R. C., Giardini, D., Panning, M., Lognonné, P., and Banerdt, B.: Seismic Moment Rate of Mars after Event S1222a, Europlanet Science Congress 2022, Granada, Spain, 18–23 Sep 2022, EPSC2022-845, https://doi.org/10.5194/epsc2022-845, 2022.

Since its deployment at the surface of Mars in 2019, the SEIS seismometer (Seismic Experiment for Interior Structure) of the NASA-InSight mission (Interior exploration using Seismic Investigations, Geodesy and Heat Transport) has detected hundreds of small magnitude seismic events. But recently, the seismic event S1222a of magnitude 4.9 which primarily contains energy above 0.1 Hz was reported by the Mars Quake Service. It is characterized by two temporally separated arrivals with a gradual beginning, a broad maximum and a very long decay lasting up to 1 hour. In this work, we propose to use seismic coda analysis on this specific event to explore the attenuation properties of Martian crust and to provide an Earth-Mars-Moon comparison.

Fig 1: Envelope characteristics of the S1222a Broadband event. The data were collected on the VBB sensors with sampling rates of 20 samples per second. (a) Base 10 logarithm of energy envelopes normalized by the noise level as a function of time and fit of the coda decay (black dashed lines). The gray dashed line indicates signal-to-noise ratio (SNR) = 2. The different colors correspond to different frequency bands indicated in inset. The time t = 0 s corresponds to the first arrival. t_P and t_S refer to the arrival times of the first and second wave packets (red dashed lines). (b) Frequency dependence of Qc in the 0.4 –7.5 Hz frequency band. The color bar indicates the SNR measured on the horizontal components. The red and black dashed line shows the power-law fit Qc = 900f^0.36and Qc = 655f^0.94 respectively (f is the frequency.).

We employ basic multiple-scattering concepts to examine and to compare the energetic characteristics of the Earth, Martian and Lunar crust. We measured the coda quality factor Q_c, that characterizes the rate of decay of the seismogram envelope, to have a first view of the attenuation characteristics [1] (see fig 1.a). We performed a fit of the frequency dependence of Q_c using a parameterization of the form Q_c = Q₀ fⁿ. Based on linear least-squares applied to the logarithm of Q_c (see fig.1.b), we obtain two very distinct regimes of Q_c as a function of the frequency f. In the [0.4 Hz - 1.5 Hz] range Q₀ = 900 and n  0.36, when at higher frequency, Q₀ = 655 and n  0.94 This result is specific to Mars and could be related to the stratification of the attenuation properties in the Martian crust. The quasi-linear frequency increase of Q_c seen in [2.5 Hz – 7.5 Hz] range suggests that the coda decay is governed by a frequency independent attenuation time. Similar values of Q₀ and n, in this frequency range, were reported by using a Very High Frequency group events [2] (see Fig 2 in orange and red).  

Fig 2:  Coda attenuation as a function of the frequency for the Earth-cratons regions (green), the Earth-tectonics regions (bleu), the Moon (gray), Mars ( α= 0 in red and α= 1 in orange) and the S1222a BB event (black).

    In the case of the regional earthquakes on Earth, the frequency dependence of Q_c is radically different between tectonic areas and shields. Q₀varies from 100 to 400 in tectonic areas (blue) [e.g. 3] and from 600 to 1000 in shields areas (green) [e.g. 4], where the frequency exponent n is equal to 1 and 0.1-0.5 respectively. [5] report coda Q_c values of 2400 at 0.5 Hz and possibly as large as 10000 at 7 Hz on the Moon (gray). This result suggests that Q₀ is a factor of 2 to 4 larger than on Mars when the frequency exponent n is more likely in the range of 0.4 – 0.6. To summarize, our Q_c measurements exhibit two different regimes of frequency dependence. At higher frequency, the Q₀ and n valuesare very close to the one found on Very High Frequency group events. The unique combination of high Q₀ with a frequency exponent n =1 also distinguishes very clearly Mars from the Earth. By the time of the meeting, we hope to present the first results of envelope modeling based on elastic radiative transport in a stratified medium to quantify the contribution of the scattering and the absorption to the total attenuation.

    References:

 [1] Aki, K. (1969). Analysis of the seismic coda of local earthquakes as scattered waves. Journal of geophysical research, 74(2), 615-631.

 [2] Menina et al. (2021), Energy Envelope and Attenuation Characteristics of High‐Frequency (HF) and Very‐High‐Frequency (VF) Martian Events, Bulletin of the Seismological Society of America (2021) 111 (6): 3016–3034.

 [3] Mayeda, K., Koyanagi, S., Hoshiba, M., Aki, K., & Zeng, Y. (1992). A comparative study of scattering, intrinsic, and coda Q− 1 for Hawaii, Long Valley, and central California between 1.5 and 15.0 Hz. Journal of Geophysical Research: Solid Earth, 97(B5), 6643-6659.

[4] Mitchell, B, J et al. (2008). A continent-wide map of 1-Hz Lg coda Q variation across Eurasia and its relation to lithospheric evolution. Journal of Geophysical Research: Solid Earth, 113(B4).

[5] Gillet, K., et al (2017). Scattering attenuation profile of the Moon: Implications for shallow moonquakes and the structure of the megaregolith. Physics of the Earth and Planetary Interiors, 262, 28-40.

How to cite: menina, S., Margerin, L., kawamura, T., Lognonné, P., Xu, Z., Onodera, K., Calvet, M., and Garcia, R.: Scattering and attenuation properties of the Martian crust through the S1222a seismic event, Europlanet Science Congress 2022, Granada, Spain, 18–23 Sep 2022, EPSC2022-805, https://doi.org/10.5194/epsc2022-805, 2022.

The InSight (Interior exploration using Seismic Investigations, Geodesy and Heat Transport) lander provides the unique opportunity to explore the seismicity and the present-day seismic structure of Mars (Banerdt et al., 2020; Lognone et al., 2020; Giardini et al. 2020). Since the deployment of the seismometer SEIS, hundreds of seismic events have been recorded, and information about these events is available in the seismic catalogs produced by the Mars Quake Service (MQS) (Clinton et al., 2021; MQS Catalog V9).

Fig. 1: Computational framework used in this study to incorporate thermal models in numerical wave simulations: Spherical harmonic Vp, Vs, and density models based on thermal simulations (e.g., Plesa et al., 2021, top-left) are expanded on a spectral-element mesh (Ciardelli et al. 2022, bottom-left) at desired resolutions. Sample synthetic 3D and 1D seismograms at 0, 90, 180, and 270-degree azimuths and 29 and 74-degree epicentral distances computed for the thermal model Case65-TAY (Plesa et al. 2021) with crustal thickness model 3200_1_DWTh2Ref1 (Wieczorek & Zuber 2004) and its average, respectively, shown on the right. Topography, rotation, ellipticity, gravity, and PREM attenuation are all taken into account during simulations with the SPECFEM3D_GLOBE package (Komatitsch & Tromp 2002). The source is the moment tensor solution of event S0235b located at the InSight lander with a higher scalar moment. The seismograms are bandpass filtered between 10-100 s. 

InSight’s seismic recordings have been used to constrain the thickness of the crust (Knapmeyer-Endrun et al., 2021), the upper mantle structure (Khan et al., 2021) and the size of the martian core (Stähler et al., 2021), which has recently been updated by Duran et al. (2022). The latest largest moment magnitude ~5 event provides an unprecedented opportunity to investigate the interior of Mars, and to constrain its seismic structure using both body and surface waves. Seismic wavespeeds can present substantial variations in particular in the lithosphere where they follow the same pattern as the crustal thickness (Plesa et al., 2021). These variations can extend to 400 km or deeper for models with a thick thermal lithosphere as suggested by the analysis of seismic events.

In this study, we investigate the 3D structural effects on the seismic body and surface waveforms, such as surface topography, crustal thickness variations, etc., and combine them with thermal evolution models using 3D seismic wave simulations (Fig. 1). We quantify the difference between 1D radially symmetric seismic models, radially symmetric models with 3D crustal thickness variations and topography, and full 3D seismic wavespeeds caused by the variations of crust-mantle interface and mantle thermal anomalies (i.e., mantle plumes) derived from thermal simulations. In our simulations, we include the latest crustal thickness models of Mars (Wieczorek et al., 2022) derived from gravity and topography data and constrained by seismic measurements from InSight.

In a first step, we use the radially symmetric structure constrained by the InSight data and similar to the study of Bozdag et al. (2017), and investigate the effect of surface topography, 3D crustal thickness variations, attenuation, and moment tensor solutions constrained by Insight data on body and surface waves. As expected, surface waveforms are most affected by the crustal dichotomy, which also highlights the importance of anelasticity in explaining multi-orbit Rayleigh and Love waves. 

In a second step, we explore the effect of a 3D mantle derived from thermal evolution models on body and surface waves. We use seismic models that are representative of various thermal conditions obtained by 3D geodynamical models. Similar to Plesa et al. (2021), we use the present-day thermal state and associated temperature variations to calculate P and S wavespeeds that are then used to simulate the seismic wave propagation. We use the 3D global wave propagation solver SPECFEM3D_GLOBE package (Komatitsch & Tromp 2002) for numerical simulations, freely available from CIG (Computational Infrastructure for Geodynamics), and compare synthetics to the prominent marsquakes, including the latest largest moment magnitude ~5 event.

References

Banerdt et al., 2020. Initial results from the InSight mission on Mars. Nature Geoscience, 13, 183– 189.

Bozdağ et al., 2017. Simulations of seismic wave propagation on Mars, Space Science Reviews, doi: 10.1007/s11214-017-0350-z.

Ciardelli et al.,  2022. SphGLLTools: A toolbox for visualization of large seismic model files based on 3D spectral-element meshes, Computers & Geosciences, 159, 105007, https://doi.org/10.1016/j.cageo.2021.105007.

Clinton et al. (2021). The Marsquake catalogue from InSight, sols 0-478. Physics of the Earth and Planetary Interiors, 310, 106595. https://doi.org/10.1016/j.pepi.2020.106595

Durán et al., “Seismology on Mars: An analysis of direct, reflected, and converted seismic body waves with implications for interior structure,” Physics of the Earth and Planetary Interiors, vol. 325, p. 106 851, 2022, issn: 0031-9201. doi: 10.1016/j.pepi.2022.106851.

Giardini et al. (2020). The seismicity of Mars. Nature Geoscience, 13(3), 205– 212.

Khan et al., “Upper mantle structure of Mars from InSight seismic data,” Science, vol. 373, no. 6553, pp. 434–438, 2021, issn: 0036-8075. doi: 10.1126/science.abf2966.

Knapmeyer-Endrun et al., 2021. Thickness and structure of the Martian crust from InSight seismic data, Science, 6553(373), 438-443, DOI: 10.1126/science.abf8966.

Komatitsch & Tromp, 2002. Spectral-element simulations of global seismic wave propagation – I. Validation, Geophysical Journal International, 149(2), 390–412.

Lognonné et al., 2019. SEIS: Insight's seismic experiment for internal structure of Mars. Space Science Reviews, 215(1), 12.

Plesa et al., 2021. Seismic Velocities Distribution in a 3D Mantle: Implications for InSight Measurements, Journal of Geophysical Research-Planets, https://doi.org/10.1029/2020JE006755.

Stähler et al., 2021 “Seismic detection of the martian core,” Science, vol. 373, no. 6553, pp. 443–448, 2021, issn: 0036- 8075. doi: 10.1126/science.abi7730.

Wieczorek & Zuber, 2004. Thickness of the martian crust: Improved constraints from geoid-to-topography ratios. J. Geophys. Res., Planets 109(E1), E01009. doi:10.1029/2003JE002153

Wieczorek et al., 2022. InSight constraints on the global character of the Martian crust. Journal of Geophysical Research: Planets, e2022JE007298.
       

How to cite: Bozdag, E., Plesa, A.-C., Huang, Q., Peter, D., Ciardelli, C., Brinkman, N., Knapmeyer, M., Ceylan, S., Wieczorek, M., Knapmeyer-Endrun, B., Rivoldini, A., Khan, A., Fernando, B., Nissen-Meyer, T., and Lognonné, P.: Effects of 3D structure on Martian seismic waves, Europlanet Science Congress 2022, Granada, Spain, 18–23 Sep 2022, EPSC2022-1221, https://doi.org/10.5194/epsc2022-1221, 2022.

NASA’s mission, InSight (Interior Exploration using Seismic Investigations, Geodesy and Heat Transport), has been conducting seismic observation since January 2019  (Banerdt et al. 2020; Lognonné et al. 2020; Giardini et al. 2020). Recently InSight has recorded a magnitude-5 event, S1222a, and in the seismic recording, Mars Quake Service identifies both Rayleigh and Love waves. Based on the two surface waves, we study the Martian subsurface radial anisotropy. We measure the group and phase velocities of the two surface waves (Figure 1), and then apply Markov chain Monte Carlo inversion to the measurement. Inversion of the Rayleigh- and Love-wave velocities provides the 1D Sv- and Sh-wave velocity models (Vsv and Vsh), respectively. The difference between Vsv and Vsh indicates the radial anisotropy distribution in depth and aids us in investigating the geological structure and/or mineralogy of the Martian subsurface.

Figure 1. The Rayleigh- and Love-wave group and phase velocity measurement (black trends) of S1222a. The Rayleigh-wave measurement is from the vertical (Z) and radial (R) component data; the Love-wave measurement is from the transverse component (T). Our group and phase velocity measurement algorithms are from Bensen et al. (2007) and Yao et al. (2006), respectively. The multiple trends in the phase-velocity subplots are due to the cycle skipping in the phase-velocity measurement.

References:

Bensen, G. D., et al. "Processing seismic ambient noise data to obtain reliable broad-band surface wave dispersion measurements." Geophysical journal international 169.3 (2007): 1239-1260.

Banerdt, W. Bruce, et al. "Initial results from the InSight mission on Mars." Nature Geoscience 13.3 (2020): 183-189.

Giardini, Domenico, et al. "The seismicity of Mars." Nature Geoscience 13.3 (2020): 205-212.

Lognonné, Philippe, et al. "Constraints on the shallow elastic and anelastic structure of Mars from InSight seismic data." Nature Geoscience 13.3 (2020): 213-220.

Yao, Huajian, Robert D. van Der Hilst, and Maarten V. De Hoop. "Surface-wave array tomography in SE Tibet from ambient seismic noise and two-station analysis—I. Phase velocity maps." Geophysical Journal International 166.2 (2006): 732-744.

How to cite: Xu, Z., Stutzmann, E., Lognonné, P., Schimmel, M., Montagner, J.-P., and Kawamura, T.: Radial anisotropy from surface-wave observation in Mars, Europlanet Science Congress 2022, Granada, Spain, 18–23 Sep 2022, EPSC2022-863, https://doi.org/10.5194/epsc2022-863, 2022.

For over three Earth years, NASA’s InSight mission has returned data from the Seismic Experiment for Interior Structure (SEIS) on Mars with over 1300 recorded marsquakes (Horleston et al., this issue), leading to numerous breakthroughs in determining the planet's structure (Khan et al., 2021; Stähler et al., 2021; Knapmeyer-Endrun et al., 2021). However, a large number of detected marsquakes are contaminated by SEIS’s complex interaction with the Martian atmosphere which injects noise and couples into the seismic signal. Identification of surface waves, polarisation analysis and clear body-wave arrivals is therefore often inhibited by this strong atmospheric noise. Despite numerous identifications, only 12 events have so far been fully located. Detection and analysis of the seismic component thus requires separation from such aseismic signal sources.

Here we present a novel denoising model which exploits the strong correlation between the ground motion and the atmospheric conditions recorded at InSight. The approach exploits linearities in the noise injected by the atmosphere by using weather data measured by the lander, or in the absence of such data due to power constraints, the excitation of wind-sensitive lander modes that have been proven an effective atmospheric proxy in estimating this injection on Mars (Charalambous et al., 2021; Clinton et al., 2021; Dahmen et al., 2021). The output results in “demodulation”, i.e., the cancellation of any environmental noise and the effective isolation of the seismic signal power in time-frequency space. Our denoising approach is thus able to remove and decouple the environmental noise from the seismic signal to reveal features in the seismic event waveform that were previously hidden in the noise.

Using the proposed denoising technique, we investigate all marsquake events above magnitude 3 to search for surface waves not identifiable with pre-existing methods. To correctly identify and associate these dispersive wave packets as Rayleigh and Love waves, we examine the characteristics of each through frequency-dependent polarisation analysis. The residual power from the denoised signal allows an accurate examination of the partitioning of energy between the horizontal and vertical components (H/V ratio) of the event. This energy isolation in turn informs an H/V-weighted polarisation analysis which enhances the characteristics of body and surface waves. This approach effectively cancels out the contribution of other polarised signals from the environment, enhancing the characteristics in the signal of interest. The residuals thus not only allow us to correctly identify surface waves, but also locate events which could not previously be located by refining back-azimuths and distances through clear P- and S-wave arrivals.

Using these results, we investigate the lateral variation in the crustal properties with events that are located close to an equatorial transect of Mars, including both major- and minor-arc detections. Events not on the transect further allow us to populate a map of lateral variations in thickness of the crust revealing geographical differences in the crustal structure, but also constraining the average thickness of the crust and the range of allowable crustal densities. This places additional constraints on the seismic velocities of the crust and lithosphere. Finally, with demodulation we further explore the identification and validation of multi-orbit Rayleigh waves and second-arriving Love wave packets from InSight’s largest marsquake recorded so far at a moment magnitude of 4.7 (S1222a), providing further constraints to the model.

References

Charalambous, C., Stott, A. E., Pike, W. T., ... & Banerdt, W. B. (2021). A Comodulation Analysis of Atmospheric Energy Injection Into the Ground Motion at InSight, Mars. Journal of Geophysical Research: Planets, 126(4).

Clinton, J. F., Ceylan, S., van Driel, ... & Stott, A. E. (2021). The Marsquake catalogue from InSight, sols 0–478. Physics of the Earth and Planetary Interiors, 310, 106595.

Dahmen, N. L., Zenhäusern, G., Clinton, ... & Banerdt, W. B. (2021). Resonances and lander modes observed by insight on Mars (1–9 Hz). Bulletin of the Seismological Society of America, 111(6), 2924-2950.

Horleston, A., Clinton, J., Ceylan, S., … Banerdt, W. B. The seismicity of Mars as recorded by InSight’s Marsquake Service, this issue

Khan, A., Ceylan, S., van Driel, M., ... & Banerdt, W. B. (2021). Upper mantle structure of Mars from InSight seismic data. Science, 373(6553), 434-438.

Stähler, S., Khan, A., Banerdt, W. B., ... & Smrekar, S. (2021). Seismic detection of the martian core. Science, 373(6553), 443-448.

Knapmeyer-Endrun, B., Panning, M. P., Bissig, F., ... & Banerdt, W. B. (2021). Thickness and structure of the martian crust from InSight seismic data. Science, 373(6553), 438-443.

How to cite: Charalambous, C., Pike, T., Fernando, B., Stott, A., Nissen-Meyer, T., and Lognonné, P.: Denoising InSight: Determination of Mars’ lateral crustal variations through surface-wave identification, Europlanet Science Congress 2022, Granada, Spain, 18–23 Sep 2022, EPSC2022-1078, https://doi.org/10.5194/epsc2022-1078, 2022.

Introduction: For a single seismometer, the most effective techniques for studying deep structure use norma mode frequencies, which do not require knowledge of the source location. Past modeling (Lognonné et al., 1996, Lognonné, 2005) suggest that Normal mode spectral peaks from 5–20 mHz (sensitive to mantle structure) are expected to have amplitude larger than the SEIS requirement (10^-9 m/s²/Hz^1/2) for moment larger than 2 10¹⁷ Nm (equivalent magnitude ∼5.5).

Even for the event 1222 from May, 4, 2022, with an estimated magnitude of 5, the amplitude of normal modes is therefore low and their identification will be challenging in the data recorded by the SEIS VBB (Lognonné et al., 2019).

Signal preprocessing: A very careful cleaning of the SEIS data is therefore requested, especially in order to remove most of the thermal glitches.  If not made, the spectra are dominated by these glitches. This pre-cleaning allow to converge to a corrected signal, which is robust with respect to deglitching parameters (Figure 1).

Figure 1: (left) raw VBBZ data and (right) deglitched VBB data. Spectra from the night following the 1222 event.

The further detection of modes relies on the identification of spectral peaks in the Fourier amplitude or power spectra. However, close to the background noise level these spectral peaks can be either due to small harmonic signals contaminated by noise or due to the fortuitous constructive interference of noise. Besides, harmonic signals may be perceptive only during a limited portion of the analyzed time series which together with other larger amplitude signals further complicate the detection. For these reasons, we employ phase autocorrelation spectra (Schimmel et al., 2018) and conduct also a Phasor-Walkout analysis (e.g., Zürn and Rydelek, 1994, Schimmel et al., 2002) to search for and evidence normal modes.  In the following, we briefly summarize these strategies.

Phase autocorrelation spectra. The identification of modes and determination of their frequency are usually based on the computation of the power or energy spectral density (PSD or ESD) which can be obtained through the Fourier Transform of differently defined autocorrelations. ESD can be computed for transient waveforms and equals the squared amplitude spectrum of the seismic recording while PSD is employed when the time series cannot be directly Fourier transformed due to infinite signal energy. Although the underlying autocorrelations are differently defined, they measure the self-similarity of the time series to reveal the spectral contents. In Schimmel et al. (2018), the conventional autocorrelations have been replaced by the phase cross-correlation (PCC; Schimmel ,1999) to show that phase autocorrelations provide an approach which can further aid the detection of modes. PCC is amplitude unbiased as it is based on envelope normalized analytic signals. Inherent to this, PCC is much less affected by outlying signals and supplies robust and independent spectra.

Phasor-Walkout. The Phasor-Walkout approach (see  Zürn and Rydelek, 1994 for a review and further references) is a graphical representation of the Fourier Transform at a given test-frequency. It pictures the construction of the amplitude spectrum as a vector summation in the complex number space. In this summation, each vector corresponds to a time sample where the sample amplitude and time provide the length of the vector and its angle or phase. The sum of all vectors equals the Fourier amplitude of the spectrum at that frequency. The “walk” of the vectors in the complex number space is called Phasor-Walkout and permits to assess how the spectral amplitude has been build up. If the vectors point into similar directions then the summation becomes constructive while random directions produce a destructive summation which depends on the individual vector amplitudes.  For harmonic signals where the test-frequency matches the frequency of the oscillation the walk is straight while for pure random noise the Phasor-Walkout produces a random walk around its start point. Thus the qualitative inspection of Phasor-Walkout patterns may reveal the origin of spectral peaks.

Fig. 2 shows an example of phasor-walkouts at four different test frequencies.  Amplitude scale
is arbitrary and each color marks a two hours segment of the data. The walk starts at (0,0) which corresponds to the MQS event origin time. The top right panel shows a random walk while the other three panels contain linear walkouts of more than 6 hours.

Figure 2: walkout for 4 test frequencies.

Further work is required to present clean walkouts validating normal modes in the signal of the largest marsquake recorded by SEIS. When achieved, these frequencies might be inverted and will provide new constraints on the interior structure of the planet (Panning et al., 2017)

References:

Lognonné, P., J. Gagnepain-Beyneix, W.B. Banerdt, S.Cacho, J.F. Karczewski, M. Morand, An Ultra-Broad Band Seismometer on InterMarsnet, Planetary Space Sciences, 44, 1237-1249, doi:10.1016/S0032-0633(96)00083-9, 1996.

Lognonné, P., Planetary seismology, Annual Review in Earth Planet. Sci., 33 :19.1-19.34, doi :10.1146/annurev.earth.33.092203.122605, 2005.

Lognonné, P., Banerdt, W.B., Giardini, D. et al., SEIS: Insight’s Seismic Experiment for Internal Structure of Mars, Space Sci Rev, 215, 12, doi : 1007/s11214-018-0574-6 , 2019.

Nishikawa, Y., Lognonné, P., Kawamura, T., Spiga, A., Stutzmann, E., Schimmel, M., Bertrand, T., Forget, F., K. Kurita, K., Mars’ Background Free Oscillations, Space Science Reviews 215:13, doi: 10.1007/s11214-019-0579-9, 2019.

Panning, M.P., P.Lognonné, W. B. Banerdt et al., Planned products of the Mars Structure Service for the InSight mission, Mars, Space Science review, 211, 611–650, doi: 10.1007/s11214-016-0317-5, 2017.

Schimmel M., Phase cross-correlations: design, comparisons and applications, Bull. Seismol. Soc. Am., 89, 1366-1378, 1999.

Schimmel M., J. Waterhouse, M.D. Marques, D. Weinert, Circadian and ultradian rhythmicities in very premature neonates maintained in incubators , Biol. Rhythm Res.,  33, 83-111, doi:10.1076/brhm.33.1.83.1321 , 2002.

Schimmel, M., Stutzmann, E., Ventosa, S., Low-frequency ambient noise autocorrelations: Waveforms and normal modes, Seismological Research Letters, 89 (4), 1488-1496, doi: 10.1785/0220180027, 2018.

Zürn, W.; Rydelek, P., Revisiting the phasor-walkout method for detailed investigation of harmonic signals in time series, Surv. in geophys. 15, 409-431, 1994.

How to cite: Lognonné, P., Schimmel, M., Stutzmann, E., Drilleau, M., Samuel, H., Panning, M., and Banerdt, W. B.: Martian free oscillations: Search in SEIS data and implications, Europlanet Science Congress 2022, Granada, Spain, 18–23 Sep 2022, EPSC2022-738, https://doi.org/10.5194/epsc2022-738, 2022.

The Farside Seismic Suite (FSS), recently selected for flight as part of the NASA PRISM (Payloads and Research Investigations on the Surface of the Moon) program and planned for flight in 2024 or 2025, would deliver two seismometers (both flight-proven through the InSight mission to Mars [Banerdt et al., 2020]) to Schrödinger Basin. The vertical Very BroadBand (VBB) seismometer is the most sensitive flight-ready seismometer ever built [Lognonné et al., 2019], while the Short Period (SP) sensor is the most sensitive and mature compact triaxial sensor available for space application [Lognonné et al., 2019]. Packaged as a self-sufficient payload, with independent power, communications and thermal control allowing survival and operation over the long lunar night, the FSS will outlive the commercial delivery lander, and provide a long-lived seismic experiment capable of answering key scientific questions (figure 1).

FSS will address three science objectives with this project:

• Investigate deep lunar structure and the difference between near and farside activity. Understanding the absence of farside seismicity recorded on Apollo seismometers [e.g. Nakamura et al., 1981] is fundamental to our understanding of the lunar deep interior. Does it reflect a nearside-farside difference in activity rate, or does seismic attenuation from partial melting in the mantle prevent observation of distant events [e.g. Weber et al., 2011]? Direct recording of farside activity, as well as possible recording of known repeating nearside moonquakes or events determined from impact flash observations will illuminate these questions.
• Understand how the lunar crust is affected by the development of an impact melt basin. Dynamic models of impact processes [e.g. Kring et al., 2016] predict deep structure beneath a well-preserved peak ring impact basin like Schrödinger Basin that can only be revealed through geophysical techniques based on receiver functions [e.g. Vinnik et al., 2011; Knapmeyer-Endrun et al., 2021] and autocorrelation of ambient noise and/or event codas [e.g. Larose et al., 2005; Compaire et al., 2020; Schimmel et al., 2021].
• Evaluate the current micrometeorite impact rate and local tectonic activity. Directly constraining micrometeorite impact rates has important implications for future lunar occupation. The lunar background seismic noise is modeled to be driven by micrometeorite impacts [Lognonné et al., 2009]. FSS will record at least 4 months of lunar background hum created by micrometeorite impacts.

In order to meet these science objectives, a series of planned measurements were defined and, based on observed Apollo-era seismicity, instrument sensitivity requirements were define. For example, in order to make an estimate of farside seismicity to address objective 1, we require recording at least 50 seismic events, and we assessed that a VBB sensitivity of 2 x 10^-10 m/s²/rtHz across the frequency band of 0.1-1Hz would be sufficient to make the measurement during the mission. This is a target, but we assess that we have ample scientific margin if the instrument is unable to reach this noise floor due to either instrument self-noise or other environmental sources of noise (figure 2). To better assess this margin, we are working to quantify the noise sources from the FSS and lander system and the lunar environment (figures 3 and 4). In current modeling, the most important noise sources for the single-component vertical VBB appear to be the instrument self-noise, the noise induced by magnetic field variations as the Moon crosses over the bowshocks surrounding the Earth’s magnetotail, and thermal tilt noise from the regolith which can couple into the VBB signal if the lander is tilted relative to the Moon’s gravitational vector. At typical levels, this noise is close to the proposed instrument requirements (figure 3), while in the stacked worst case (maximum allowed lander tilt and during a bowshock crossing), the noise may exceed the requirement by a factor of 2-3, which still should leave margin for achieving science objectives.

FSS is designed to answer several key lunar science questions from a single station on the farside of the Moon. The FSS will also allow for key technical advancement and risk reduction for future missions, such as the Lunar Geophysical Network, a candidate New Frontiers 5 mission [Neal et al., 2020]. Because deployment increases cost and complexity, assessing the need for deployment and characterizing the lander seismic noise environment will be key. In addition, measuring the lunar seismic noise floor beyond what was possible with the Apollo data will permit better requirements definition for future lunar seismic missions and future astrophysics observatories sensitive to lunar ground stability.

Figure 1: FSS will return data with unprecedented sensitivity from Schrödinger Crater over multiple lunar diurnal cycles after outliving the delivery lander. Figure adapted from Wieczorek (2009) and Kring et al. (2016).

Figure 2: Expected number of events recorded through the mission at various VBB sensitivity levels based on Apollo observation rates, with 95% confidence bounds based on Poisson statistics. Requirements are easily met if VBB performance is within a factor of 5 of the requirement.

Figure 3: Expected summed noise sources for a “typical” environment with magnetic noise away from the bowshock crossing and a lander tilt of 5 degrees (half the allowed maximum tilt).

Figure 4: Expected worst case instrument noise with magnetic noise at the bowshock crossing and lander tilt at the maximum allowed (10 degrees).

How to cite: Panning, M., Kedar, S., Bowles, N., Calcutt, S., Drilleau, M., Garcia, R., Kawamura, T., Lognonné, P., Mimoun, D., Nunn, C., Pike, W. T., Portela-Moreira, D., de Raucourt, S., Weber, R., and Wilhelm, A.: Farside Seismic Suite (FSS): First-ever seismology on the farside of the Moon and a model for long-lived lunar science, Europlanet Science Congress 2022, Granada, Spain, 18–23 Sep 2022, EPSC2022-672, https://doi.org/10.5194/epsc2022-672, 2022.