Wind measurements from landed missions on Mars are vital to characterise the near surface atmospheric behaviour on Mars and improve atmospheric models. These winds are responsible for aeolian change and the mixing of dust in and out of the atmosphere, which has a significant effect on the global circulation. The NASA InSight mission successfully recorded wind data for around 750 sols. The seismometer, however, recorded nearly continuous data for around 1400 sols. The dominant source of energy in the seismic data is in fact due to the wind. To this end, we propose a machine learning model, dubbed WindSightNet, to map the seismic data to wind speed and direction. This converts the atmospheric information in the seismic data into a physically meaningful wind signal which can be used for analysis. We retrieve wind data from the entire period the seismometer was recording which enables a comparison of the year-to-year wind variations at InSight. The continuous nature of the dataset also enables the extraction of information on baroclinic activity at long periods and the periodicity of observed convective cells. A data science based metric is proposed to provide a quantification of the year-to-year differences in the wind speeds, which highlights variations linked to dust activity as well as other transient differences worthy of further study. On the whole, the seismic-derived winds confirm the dominance of the global circulation on the winds leading to highly repeatable weather patterns.

We report observations of Rayleigh waves that orbit around Mars up to three times following the S1222a marsquake. Averaging these signals, we find the largest amplitude signals at 30 s and 85 s central period, propagating with distinctly different group velocities of 2.9 km/s and 3.8 km/s, respectively. The group velocities constraining the average crustal thickness beneath the great circle path rule out the majority of previous crustal models of Mars that have a >200 kg/m3 density contrast across the dichotomy. We find that the thickness of the martian crust is 42-56 km on average, and thus thicker than the crusts of the Earth and Moon. Together with thermal evolution models, a thick martian crust suggests that the crust must contain 50-70% of the total heat production to explain present-day local melt zones in the interior of Mars.

The properties of the first kilometers of the Martian atmospheric Planetary Boundary Layer have until now been measured by only a few instruments and probes. InSight offers an opportunity to investigate this region through seismoacoustics. On six occasions, its seismometers recorded short low-frequency waveforms, with clear dispersion between 0.4 and 4Hz. These signals are the air-to-ground coupling of impact-generated infrasound, which propagated in an low-altitude atmospheric waveguide. Their group velocity depends on the structure of effective sound speed in the boundary layer. Here, we conduct a Bayesian inversion of effective sound speed up to 2000m altitude using the group velocity measured for events S0981c, S0986c and S1034a. The inverted effective sound speed profiles are in good agreement with estimates provided by the Mars Climate Database. Differences between inverted and modeled profiles can be attributed to a local wind variation in the impact → station direction, of amplitude smaller than 2m/s.

The relatively unconstrained internal structure of Venus is a missing piece in our understanding of the Solar System formation and evolution. To determine the seismic structure of Venus’ interior, the detection of seismic waves generated by venusquakes is crucial, as recently shown by the new seismic and geodetic constraints on Mars’ interior obtained by the InSight mission. In the next decades multiple missions will fly to Venus to explore its tectonic and volcanic activity, but they will not be able to conclusively report on seismicity or detect actual seismic waves. Looking towards the next fleet of Venus missions in the future, various concepts to measure seismic waves have already been explored in the past decades. These detection methods include typical geophysical ground sensors already deployed on Earth, the Moon, and Mars; pressure sensors on balloons; and airglow imagers on orbiters to detect ground motion, the infrasound signals generated by seismic waves, and the corresponding airglow variations in the upper atmosphere. Here, we provide a first comparison between the detection capabilities of these different measurement techniques and recent estimates of Venus’ seismic activity. In addition, we discuss the performance requirements and measurement durations required to detect seismic waves with the various detection methods. As such, our study clearly presents the advantages and limitations of the different seismic wave detection techniques and can be used to drive the design of future mission concepts aiming to study the seismicity of Venus.

InSight’s seismometers recorded more than 1300 events. Ninety-eight of these, named the low-frequency family, show energy predominantly below 1 Hz down to ∼0.125 Hz. The Marsquake Service identified seismic phases and computed distances for 42 of these marsquakes, 26 of which have backazimuths. Hence, the locations of the majority of low-frequency family events remain undetermined. Here, we use an envelope shape similarity approach to determine event classes and distances, and introduce an alternative method to estimate the backazimuth. In our similarity approach, we use the highest quality marsquakes with well-constrained distance estimates as templates, including the largest event S1222a, and assign distances to marsquakes with relatively high signal-to-noise ratio based on their similarities to the template events. The resulting enhanced catalog allows us to re-evaluate the seismicity of Mars. We find the Valles Marineris region to be more active than initially perceived, where only a single marsquake (S0976a) had previously been located. We relocated two marsquakes using new backazimuth estimates, which had reported distances of ∼90o, in the SW of the Tharsis region, possibly at Olympus Mons. In addition, two marsquakes with little or no S-wave energy have been located in the NE of the Elysium Bulge. Event epicenters in Cerberus Fossae follow a North-South trend due to uncertainties in location, while the fault system is in the NW-SE direction; therefore, these events are re-projected along the observed fault system.

One of the future NASA space program includes the Farside Seismic Suite (FSS) payload, a single station with two seismometers, on the far side of the Moon. During FSS operations, the processing of the data will provide us with new insight into the Moon’s seismic activity. One of Apollo mission finding is the existence of deep moonquakes (DMQ), and the nature of their temporal occurrence patterns as well as the spatially clustering. It has been shown that DMQs reside in about 300 source regions. In this paper we tackle how we can associate new events with these source regions using the single station data. We propose a machine learning model that is trained to differentiate between DMQ nests using only the lunar orbital parameters related to DMQ time occurrences. We show that ML models perform well (with an accuracy >70%) when they are trained to classify less than 4 nests.

NASA InSight mission revealed that Mars is seismically active today and identified a region with high activity called Cerberus Fossae. Since then, the origin of marsquakes has been an important question in Mars seismology. On 2022/05/04, InSight seismometer detected the largest marsquake during the mission, named S1222a. The source was estimated to be outside the Cerberus Fossae and it would be important to constrain the source parameters of this remarkable event. In this study, we estimate the source parameters of S1222a through spectral analyses. Since we found that seismic spectra of S1222a are heavily contaminated, we performed Empirical Green’s Function analyses. We found that the corner frequencies are 0.34-0.54 Hz for the P wave and 0.17-0.24 Hz for the S wave. These values are slightly higher than those of seismic events at Cerberus Fossae, which implies a faster rupture at the source and different origin for S1222a.

Convective vortices (whirlwinds) and dust devils (dust-loaded vortices) are one of the most common phenomena on Mars, observable on a daily basis. They reflect the local thermodynamical structure of the atmosphere and are the driving force of the dust cycle. Additionally, they cause an elastic ground deformation, which is useful for retrieving the subsurface rigidity. Therefore, investigating Martian convective vortices with the right instrumentation can lead to a better understanding of the Martian atmospheric structures as well as the subsurface physical properties. In this study, we quantitatively characterized the convective vortices detected by NASA’s InSight mission (~13,000 events) using both meteorological (e.g., pressure, wind speed, temperature) and seismic data. The evaluated parameters, such as the signal-to-noise ratio, event duration, asymmetricity of pressure drop profiles, and cross-correlation coefficient between seismic and pressure signals, are compiled as a catalog. To demonstrate how our catalog can contribute to scientific investigations, we show two analysis results about (i) asymmetrical features seen in the vortex-related pressure drops and (ii) atmospheric-ground interaction to retrieve the subsurface physical properties.

A document by Sebastián Carrasco. Click on the document to view its contents.

On May 4th, 2022 the InSight seismometer SEIS recorded the largest marsquake ever observed, S1222a, with an initial magnitude estimate of Mw 4.7. Understanding the depth and source properties of this event has important implications for the nature of tectonic activity on Mars. Located ~37 degrees to the southeast of InSight, S1222a is one of the few non-impact marsquakes that exhibits prominent ratio surface waves. We use waveform modeling of body waves (P and S) and surface waves (Rayleigh and Love) to constrain the moment tensor and quantify the associated uncertainty. We find that S1222a likely resulted from dip-slip faulting in the mid-crust (source depth ~18 – 28 km) and estimate a scalar moment of 3.51015 – 5.01015 Nm (magnitude Mw 4.3 – 4.4). The best-fitting focal mechanism is sensitive to the choice of phase windows and misfit weights, as well as the structural model of Mars used to calculate Green’s functions. We find that an E-W to SE-NW striking thrust fault can explain the data well, although depending on the choice of misfit weighting, a normal fault solution is also permissible. The orientation of the best-fitting fault plane solutions suggests that S1222a takes place on a fault system near the martian crustal dichotomy accommodating relative motion between the northern lowlands and southern highlands. Independent constraints on the event depth and improved models of the (an)isotropic velocity structure of the martian crust and mantle could help resolve the ambiguity inherent to single-station moment tensor inversions of S1222a and other marsquakes.

On May 4th 2022, the seismometer on Mars observed the largest marsquake (S1222a) during its operation. One of the most specific features of S1222a is the long event duration lasting more than 8 hours from the occurrence, in addition to the clear appearance of body and surface waves. As demonstrated on Earth, by modeling a long-lasting and scattered surface wave with the radiative transfer theory, we estimated the scattering and intrinsic quality factors of Mars (Qs and Qi). This study especially focused on the frequency range between 0.05 - 0.09 Hz, where Qs and Qi have not been constrained yet. Our results revealed that Qi = 1000 - 1500 and Qs = 30 - 500. By summarizing the Martian Qi and Qs estimated so far and by comparing them with those of other celestial bodies, we found that, overall, the Martian scattering and absorption properties showed Earth-like values.

A document by Antoine Lucas. Click on the document to view its contents.