TP17

With the deployment of a seismometer on the surface of Mars as part of NASA’s InSight mission,
the Seismic Experiment for Interior Structure (SEIS) has been collecting continuous data since early 2019.
The primary goal of InSight is to improve our understanding of the internal structure and dynamics of Mars, in
particular crust, mantle, and core. Here we describe constraints on the structure of the mantle of Mars based
on inversion of seismic body wave arrivals from a number of low-frequency marsquakes.

We consider 8 of the largest (moment magnitude is estimated to be between 3 and 4) low-frequency events with
dominant energy below 1 Hz for which P- and S-waves are identifiable, enabling epicentral distance estimation.
The 8 events occur in the distance range 25-75 degrees. Body wave arrivals that include the main P- and S-waves,
surface reflections (PP, PPP, SS, SSS), and core reflections (ScS) are picked using a set of complimentary methods
that allows to check for consistency. The resultant set of differential travel times (PP-P, PPP-P, SS-S,...) are
subsequently inverted for radial profiles of seismic P- and S-wave velocity, core size and mean density, and epicentral
location of the events. To determine interior structure, we rely on independent methods as a means of assessing the
robustness of the results.

We present a radial velocity model for the upper mantle of Mars, with implications for the thermo-chemical evolution
of the planet that match a cooling, differentiated body, and a thick lithosphere. Based on the location of the events,
we are able to constrain structure to the core-mantle-boundary, including the size of the core and its mean
density that point to large liquid and relatively light core, implying a significant complement of light alloying
elements. Our estimate of the average crustal thickness as seen by all events is compatible with the local crustal
thickness at the InSight landing determined from observations of converted phases.

How to cite: Khan, A. and the the InSight team: Crust, mantle and core structure of Mars from InSight seismic data, Europlanet Science Congress 2021, online, 13–24 Sep 2021, EPSC2021-512, https://doi.org/10.5194/epsc2021-512, 2021.

The scattering of seismic waves is the signature of random heterogeneities, present in the lithospheric structure of a terrestrial planet. It is the result of refraction and reflection of the seismic waves generated by a quake, when they cross materials with different shear rigidity, bulk modulus, and density and therefore different seismic wave velocities, compared to the ambient space.  On Earth, the seismic waves show relatively weak scattering, identified in later arriving coda waves that follow the main arrivals of body waves and decay with time. In contrast, seismic wave scattering is much more significant on the Moon, where the high heterogeneous structure of the lunar megaregolith, produced through millions of years of impact bombardment, is a structure that creates an extreme scattering environment.

The landing of the NASA InSight mission on Mars in 2018, which carried and deployed a seismometer for the first time on the Martian ground, offered a pristine dataset for the investigation and analysis of the characteristics of the scattering attenuation of the Martian crust and uppermost mantle which is important for understanding the structure of the Martian interior. Lognonné et al. (2020) used a methodology based on the radiative transfer model (Margerin et al., 1998) to offer the first constraints for the scattering and attenuation in the Martian crust. In this study, we performed a further examination based on more and newer events of the Martian Seismic Catalog (InSight Marsquake Service, 2021).

The Marsquake Catalog contains events that are categorized according to the frequency content of the seismic signal (Clinton et al., 2021). In this study, we used 19 events of 4 different families, namely the Low Frequency, Broadband, High Frequency, and Very High Frequency events, for our investigation. We focused our investigation on the characteristics of the S-coda waveforms and for this reason, we worked on the respective energy envelopes. We manually picked the envelopes, defining the time window of the S-coda waves, as well the frequency range for each event, directly from the spectrograms of the events' signals, using an appropriately developed visual tool.

We used a modeling approach (Dainty et al., 1974) that was developed for the computation of the energy envelopes of shallow events (Lunar impacts) and a diffusive, highly scattering layer, sitting over an elastic half-space. The energy envelope depends on the thickness of the diffusive layer, the range of the seismic ray, the diffusivity and the attenuation in the top layer, and the seismic wave velocity in the underneath elastic half-space. We analyzed all the tradeoffs between the terms of the modeling equation, namely the geometrical relationship of the velocity contrast between the diffusive layer and the elastic half-space with the seismic ray range and the diffusive layer thickness, the diffusivity with the diffusive layer thickness, and between the diffusivity and the velocity contrast of the two examined layers.

The presence of the aforementioned tradeoffs made the definition of a unique model a very hard task, as the information for the azimuthal characteristics of the signal is not available for the examined events. This is a limitation that exists in seismology only while working with one station, with the InSight seismometer being the only station on a planet, and the amplitude of the seismic signal is not big enough to perform a specific polarization analysis and derive the azimuthal origin of the recorded signal. For this reason, we reviewed the fit between the modeling and the data, depending on the frequency content of the events.

The Low Frequency and the Broadband events, which have a frequency content mainly below the tick noise detected at 1 Hz, could not satisfy the modeling approach of a simple diffusive layer. The spectral envelopes of the S-coda waves of these events are decaying very rapidly, which suggests an origin in a more elastic environment. This is in agreement with previous studies (Giardini et al., 2020) that suggest that these events are generated deeper in the Martian mantle. For this reason, we applied another approach to these signals, with an energy envelope equation designed for deep moonquakes (Dainty et al.,1974), but it was not either capable to fit the examined data envelopes, suggesting the absence of a very thick megaregolith structure on Mars.

Based on the results of the High Frequency (HF) and Very High Frequency (VF) events we observed a range of possible paths and diffusivities that can satisfy the data and we investigated the tradeoffs between the parameters of a modeling equation that control the shape of the energy envelope for the events. The analysis of these tradeoffs does not permit us to make any assumptions about the depth of the diffusive region in the Martian crust and the upper mantle as their azimuthal characteristics are unknown and therefore it is not feasible to tell if the difference in the result analysis reflects vertical or lateral variations of the uppermost diffusive layer in the Martian lithosphere.

The results of this study illustrate one of the challenges in working with single-station seismic data where event location information, including distance, azimuth, and depth are crucial for understanding the lateral variation in seismic properties of a planet. The existence of a seismic network on the planetary scale will improve the ability of phase peaking and location identification of the events and therefore it will give additional constraints for a similar analysis.

References

Clinton, J. F. et al. (2021). The Marsquake catalogue from InSight, sols 0–478.Phys Earth Planet In, 310:106595.

Dainty,  A. M. et al. (1974). Seismic scattering and shallow structure of the Moon in Oceanus Procellarum.The Moon,9(1-2):11–29.

Giardini, D. et al. (2020). The seismicity of Mars.Nat Geosci, 13(3):205–212.InSight Marsquake Service (2021). 

Mars Seismic Catalogue, InSight Mission; V5 2021-01-04.

Lognonné, P. et al. (2020).  Constraints on the shallow elastic and anelastic structure of Mars from InSight seismic data.NatGeosci, 13(3):213–220.

Margerin, L. et al. (1998).  Radiative transfer and diffusion of waves in a layered medium: new insight into coda Q.GeophysJ Int, 134(2):596–612.

How to cite: Karakostas, F., Schmerr, N., Maguire, R., Huang, Q., Kim, D., Lekić, V., Margerin, L., Nunn, C., Menina, S., Kawamura, T., Lognonné, P., Giardini, D., and Banerdt, B.: An analysis of the seismic scattering on Mars, using the InSight seismic data, Europlanet Science Congress 2021, online, 13–24 Sep 2021, EPSC2021-258, https://doi.org/10.5194/epsc2021-258, 2021.

Abstract

Radar data interpretation for subsurface analysis is based upon the comparison with simulations, to discriminate a subsurface echo from a surface reflector. Our goal is to detect subsurface ice at Mars’s midlatitudes — that have been shown to be present in the first tens of meters below the surface [1]—, using data from the SHAllow RADar (SHARAD) onboard Mars Reconnaissance Orbiter (MRO).

We are specifically interested in looking into the southern midlatitudes of Mars, a highly craterized area with a variety of structures on the surface. This is why we have to be extra careful in the radar data analysis with the simulations, and we are faced with issues that are not necessarily present for the study of the poles.

We will present a method that allows us to complete the simulations and helps us to resolve further ambiguities.

Sounding the close subsurface of Mars’s midlatitudes

The MARSIS and SHARAD Nadir-looking radars have been sounding the surface of Mars since respectively 2005 and 2006, at different frequencies — between 1.8 and 5 MHz for MARSIS, and 20 MHz for SHARAD [2]—. Their data have allowed us to better understand the composition and structure of the Martian subsurface.

On fathom radar profiles, echo coming from the subsurface from nadir could arrive with the same delay that surface echoes arriving from a slant direction. This ambiguity is classically resolved by simulation of the measurement from Digital Terrain Models (DTMs) as developed for MARSIS [3,4]: given a surface model and an orbitography, we can simulate the surface-related component of the signal. By comparing the radar data to the simulation, we can therefore — in theory — fully identify the subsurface reflectors.

The question is especially complex when the objective is to detect ice at Martian midlatitudes at depths lower than a few tens of meters. Furthermore, given that the radar signal mostly comes from the nadir and a limited area around, we will have a high sensibility for the variations in local slopes in the surface models used for the simulations.

In order to accurately simulate the surface component of the signal, the surface model resolution must be in the order of the wavelength or lower (15m for the case of SHARAD). This is why we will need higher resolution DTMs than the one mainly used for MARSIS simulations (MOLA). DTMs can be generated from different data types, but to have high resolution  models, the privileged method is stereo photogrammetry [5]. As an example, with the CTX camera onboard MRO, we can reach a typical resolution of 12 meters, compared to the 463 meters of MOLA.

The area that we are focusing on is located in Terra Cimmeria, around (172°E ; 39°S), and shows a number of canal-like structures, along with numerous craters. Those structures are located a few degrees off-nadir, therefore very close to the main surface echo, complicating the analysis with the simulations. In fact, a slight variation of the model from the actual topography can change the result of the simulation, and the process of stereo photogrammetry introducing noise, we will necessarily be faced with some differences between simulations and radar data. Another issue is that on this specific area, only MOLA and HRSC DTMs are available, with a maximum resolution of 200m per pixel, far from the 15m of the wavelength.

With the aforementioned issues, we are left with ambiguities that we cannot resolve as is, so we must find a way to get around it. We will therefore present a method that allows to complete the simulation by resolving the remaining ambiguities. This method applied to the area discussed above allowed us to study in details the echoes analyzed by [6] and to revisit the results.

References

[1] C. M. Stuurman et al., « SHARAD detection and characterization of subsurface water ice deposits in Utopia Planitia, Mars: SHARAD DETECTION OF ICE UTOPIA PLANITIA », Geophys. Res. Lett., vol. 43, n^o 18, p. 9484‑9491, sept. 2016

[2] R. Seu et al., « SHARAD sounding radar on the Mars Reconnaissance Orbiter », J. Geophys. Res., vol. 112, n^o E5, p. E05S05, may 2007

[3] J. -. Nouvel, A. Herique, W. Kofman and A. Safaeinili, "Radar signal simulation: Surface modeling with the Facet Method," in Radio Science, vol. 39, no. 1, pp. 1-17, Feb. 2004

[4] Y. Berquin, A. Herique, W. Kofman, et E. Heggy, « Computing low-frequency radar surface echoes for planetary radar using Huygens-Fresnel’s principle: COMPUTING RADAR SURFACE ECHOES », Radio Sci., vol. 50, n^o 10, p. 1097‑1109, oct. 2015

[5] R. A. Beyer, O. Alexandrov, et S. McMichael, « The Ames Stereo Pipeline: NASA’s Open Source Software for Deriving and Processing Terrain Data », Earth and Space Science, vol. 5, n^o 9, p. 537‑548, sept. 2018

[6] S. Adeli, E. Hauber, G. G. Michael, P. Fawdon, I. B. Smith, et R. Jaumann, « Geomorphological Evidence of Localized Stagnant Ice Deposits in Terra Cimmeria, Mars », J. Geophys. Res. Planets, p. 2018JE005772, june 2019

How to cite: Desage, L., Herique, A., and Kofman, W.: The first tens of meters of the Martian midlatitude subsurface: How to analyze SHARAD signal?, Europlanet Science Congress 2021, online, 13–24 Sep 2021, EPSC2021-465, https://doi.org/10.5194/epsc2021-465, 2021.

Context On February 18^th 2021, the Perseverance rover (NASA Mars 2020 mission) landed at Jezero crater, an ancient open-basin lake system measuring 50 Km in diameter. More precisely, the rover landed at the Octavia E. Butler Landing Site, which is located East of the delta in a dark crater floor unit, interpreted as the Undifferentiated Smooth Unit, between two distinct units: “Crater Floor Fractured 1” and “Crater Floor Fractured Rough” units. Since the landing, the rover has traveled around 40 m North and 60 m East from its initial location. From CRISM orbital data, this region is mainly pyroxene-bearing, with some olivine and hydrated minerals [1-3].

On-board the Perseverance rover, the SuperCam instrument [4-5] is being  used as a remote-sensing facility to analyze rocks and soils targets. SuperCam is a suite of five coaligned techniques: just like ChemCam (onboard MSL/Curiosity rover on Mars since 2012), it uses the Laser Induced Breakdown Spectroscopy (LIBS) technique to determine the elementary composition of the targets, but it also uses Raman (for the first time in planetary science) and visible-infrared (VISIR - for the first time in situ) spectroscopic methods in order to access some mineralogical and structural information. A microphone gives access to some physical parameters of the sampled rocks (such as hardness [6]) as well as to some atmospheric parameters (wind direction [7]). These chemical and mineralogical analyses are contextualized thanks to a color remote micro-imager (RMI). In this study, we focus mainly on the LIBS results obtained so far.

Method We have used all the processed LIBS spectra acquired since the landing. Spectra have been processed with the following steps: denoise, background removal, wavelength calibration.

The SuperCam/LIBS technique can predict major elements such as SiO₂, TiO₂, Al2O₃, FeO, MgO, CaO, Na₂O, and K₂O. The quantification of the LIBS data is achieved based on multivariate models, trained on more than 1000 spectra from 334 different known samples acquired in laboratory before launch [8-9]. Minor elements will be quantified in the future, and for now only peak areas are used. In this study we are using images from the SuperCam instrument [10] and from the MastCam-Z and Navcam cameras [11].

Results From the images, rocks in the workspace are presenting mainly two types of morphologies (Figure 1). Besides some float rocks, there are low-standing light-toned rocks called “pavers”, and high-standing dark-toned rocks. Pavers tend to have a more important dust cover, as they are in low-relief, compared to the high-standing rocks. The relation between the pavers and the high-standing rocks is not clear. Only a few images seem to show that the high standing rocks are directly related to the pavers, i-e that they correspond to the same material.

The LIBS elemental compositions are indeed essentially similar between these two types of morphologies (Figure 2). Some elements show extreme values, such as CaO, MgO and FeO, but these elevated values are observed in both morphologies and seem to be related to particular phases that have been sampled in both types of rocks.

One important observation concerns the point-to-point dispersion among each rock, whatever the rock type. This shows that the granulometry of these rocks is quite important, with grains of at least 300 microns (which is the LIBS spot size). Several grains have indeed been observed in the RMI and Watson images, consistent with this hypothesis.