Constraining Martian Regolith and Vortex Parameters From Combined Seismic and Meteorological Measurements

The InSight mission landed on Mars in November 2018 and has since observed multiple convective vortices with both the high performance barometer and the low-noise seismometer SEIS that has unprecedented sensitivity. Here, we present a new method that uses the simultaneous pressure and seismic measurements of convective vortices to place constraints on the elastic properties of the Martian subsurface and the Martian vortex properties, while also allowing a reconstruction of the convective vortex trajectories. From data filtered in the (0.02–0.3 Hz) frequency band, we estimate that the mean value of η (η = E/[1 − ν2], where E is the Young's modulus and ν is the Poisson's ratio) of the Martian ground in the region around SEIS is 239 ± 140 MPa. In addition, we suggest that the previously reported paucity of vortex seismic observations to the west of InSight may be due to the fact that the ground is harder to the west than to the east, consistent with geomorphological surface interpretations

Autocorrelation of the Ground Vibrations Recorded by the SEIS‐InSight Seismometer on Mars

Since early February 2019, the SEIS (Seismic Experiment for Interior Structure) seismometer deployed at the surface of Mars in the framework of the InSight mission has been continuously recording the ground motion at Elysium Planitia. In this study, we take advantage of this exceptional data set to put constraints on the crustal properties of Mars using seismic interferometry (SI). To carry out this task, we first examine the continuous records from the very broadband seismometer. Several deterministic sources of environmental noise are identified and specific preprocessing strategies are presented to mitigate their influence. Applying the principles of SI to the single-station configuration of InSight, we compute, for each Sol and each hour of the martian day, the diagonal elements of the time-domain correlation tensor of random ambient vibrations recorded by SEIS. A similar computation is performed on the diffuse waveforms generated by more than a hundred Marsquakes. A careful signal- to-noise ratio analysis and an inter-comparison between the two datasets suggest that the results from SI are most reliable in a narrow frequency band around 2.4 Hz, where an amplification of both ambient vibrations and seismic events is observed. The average autocorrelation functions (ACFs) contain well identifiable seismic arrivals, that are very consistent between the two datasets. Interpreting the vertical and horizontal ACFs as, respectively, the P- and S- seismic reflectivity below InSight, we propose a simple stratified velocity model of the crust, which is mostly compatible with previous results from receiver function analysis. Our results are discussed and compared to recent works from the literature

The interior of Mars as seen by InSight (Invited)

InSight is the first planetary mission dedicated to exploring the whole interior of a planet using geophysical methods, specifically seismology and geodesy. To this end, we observed seismic waves of distant marsquakes and inverted for interior models using differential travel times of phases reflected at the surface (PP, SS...) or the core mantle-boundary (ScS), as well as those converted at crustal interfaces. Compared to previous orbital observations1-3, the seismic data added decisive new insights with consequences for the formation of Mars: The global average crustal thickness of 24-75 km is at the low end of pre-mission estimates5. Together with the the thick lithosphere of 450-600 km5, this requires an enrichment of heat-producing elements in the crust by a factor of 13-20, compared to the primitive mantle. The iron-rich liquid core is 1790-1870 km in radius6, which rules out the existence of an insulating bridgmanite-dominated lower mantle on Mars. The large, and therefore low-density core needs a high amount of light elements. Given the geochemical boundary conditions, Sulfur alone cannot explain the estimated density of ~6 g/cm3 and volatile elements, such as oxygen, carbon or hydrogen are needed in significant amounts. This observation is difficult to reconcile with classical models of late formation from the same material as Earth. We also give an overview of open questions after three years of InSight operation on the surface of Mars, such as the potential existence of an inner core or compositional layers above the CM

Constraints on the shallow elastic and anelastic structure of Mars from InSight seismic data

Mars’s seismic activity and noise have been monitored since January 2019 by the seismometer of the InSight (Interior Exploration using Seismic Investigations, Geodesy and Heat Transport) lander. At night, Mars is extremely quiet; seismic noise is about 500 times lower than Earth’s microseismic noise at periods between 4 s and 30 s. The recorded seismic noise increases during the day due to ground deformations induced by convective atmospheric vortices and ground-transferred wind-generated lander noise. Here we constrain properties of the crust beneath InSight, using signals from atmospheric vortices and from the hammering of InSight’s Heat Flow and Physical Properties (HP3) instrument, as well as the three largest Marsquakes detected as of September 2019. From receiver function analysis, we infer that the uppermost 8–11 km of the crust is highly altered and/ or fractured. We measure the crustal diffusivity and intrinsic attenuation using multiscattering analysis and find that seismic attenuation is about three times larger than on the Moon, which suggests that the crust contains small amounts of volatiles

Seismic Interferometry applied to the data of the SEIS seismometer aboard the NASA Discovery InSight mission : Crustal structure and monitoring

Le sismomètre SEIS (Seismic Experiment for Interior Structure) a été déposé à la surface de la planète Mars le 19 décembre 2018 dans le cadre de la mission NASA Discovery InSight (Interior Exploration using Seismic Investigations, Geodesy and Heat Transport). Ses objectifs sont d'explorer la structure interne et l'activité sismique de Mars.Dans cette thèse nous analysons les données transmisent par SEIS sous le prisme de l'interférométrie sismique. Cette technique tire parti des propriétés des champs diffus tels que la coda sismique ou le bruit ambiant pour reconstruire la réponse impulsionnelle du milieu par corrélation d'enregistrements sismiques. L'exceptionnelle sensibilité du sismomètre SEIS rend possible l'étude des caractéristiques du bruit ambiant martien, qui nous étaient inconnues jusqu'alors.En comparant des fonctions d'auto-corrélations de bruit et de coda d'événements sismiques martiens nous avons identifié deux régions du spectre où s'observe le bruit microsismique martien.Une amplification locale du sol autour de 2.4 Hz présente une structure spectrale que nous avons pu relier à la structure crustale de Mars. La réponse en réflexion reconstruite par auto-corrélation a permis de détecter deux interfaces crustales, à ~9 et ~24 km de profondeur, cohérentes avec les fonctions récepteur.Nous montrons également que les composantes horizontales du sismomètre contiennent la signature d'une variation saisonnière des vitesses sismiques dans leurs spectres à hautes fréquences (> 5 Hz). Ces variations, observées également dans la coda de multiplets sismiques hautes fréquences, ont pu être reliées à une réponse thermo-élastique de la subsurface sous l'effet des changements saisonnier du forçage thermique solaire. Cette observation fournie une opportunité de sonder les paramètres thermiques et élastiques de la subsurface martienne jusqu'à plus de 20 mètres de profondeur.The Seismic Experiment for Interior Structure (SEIS) seismometer was deposited on the surface of Mars on December 19, 2018 as part of the NASA Discovery InSight (Interior Exploration using Seismic Investigations, Geodesy and Heat Transport) mission. Its objectives are to explore the internal structure and seismic activity of Mars.In this thesis we analyze the data transmitted by SEIS in the light of seismic interferometry. This technique takes advantage of the properties of diffuse fields such as seismic coda or ambient noise to recover the impulse response of the medium by correlation of seismic records.The exceptional sensitivity of the SEIS seismometer makes it possible to study the characteristics of the Martian ambient noise, which were unknown to us until now.By comparing autocorrelation functions of seismic ambient noise and Marsquake coda we have identified two regions of the spectrum where Martian microseismic noise is observed.A local ground amplification around 2.4 Hz presents a spectral structure that we were able to link to the crustal structure of Mars. The reflection response reconstructed by auto-correlation allowed us to detect two crustal interfaces, at ~9 and ~24 km depth, consistent with receiver functions analysis.We also show that the horizontal components of the seismometer contain the signature of a seasonal variation of seismic velocities in their high frequency spectra (> 5 Hz).These variations, also observed in the coda of high frequency seismic multiplets, could be related to a thermo-elastic response of the subsurface under the effect of seasonal changes in solar thermal forcing.This observation provides an opportunity to probe the thermal and elastic parameters of the Martian subsurface to a depth of over 20 meters

Bayesian evidential learning : an alternative to hydrogeophysical coupled inversion

Deterministic geophysical inversion suffers from a lack of realism because of the regularization, while stochastic inversion allowing for uncertainty quantification is computationally expensive. In this contribution, we propose to use Bayesian Evidential Learning as an alternative to hydrogeophysical coupled inversion. We demonstrate the ability of the approach to successfully predict a hydrogeological target from time-lapse ERT data in the context of a heat injection and storage experiment

Comparing Well and Geophysical Data for Temperature Monitoring Within a Bayesian Experimental Design Framework

International audienceTemperature logs are an important tool in the geothermal industry. Temperature measurements from boreholes are used for exploration, system design, and monitoring. The number of observations, however, is not always sufficient to fully determine the temperature field or explore the entire parameter space of interest. Drilling in the best locations is still difficult and expensive. It is therefore critical to optimize the number and location of boreholes. Due to its higher spatial resolution and lower cost, four-dimensional (4D) temperature field monitoring via time-lapse Electrical Resistivity Tomography (ERT) has been investigated as a potential alternative. We use Bayesian Evidential Learning (BEL), a Monte Carlo-based training approach, to optimize the design of a 4D temperature field monitoring experiment. We demonstrate how BEL can take into account various data source combinations (temperature logs combined with geophysical data) in the Bayesian optimal experimental design (BOED). To determine the optimal data source combination, we use the Root Mean Squared Error (RMSE) of the predicted target in the low dimensional latent space where BEL is solving the prediction problem. The parameter estimates are accurate enough to use in BOED. Furthermore, the method is not limited to monitoring temperature fields and can be applied to other similar experimental design problems. The method is computationally efficient and requires little training data. For the considered optimal design problem, a training set of only 200 samples and a test set of 50 samples is sufficient