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.

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.

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 15 January 2022 eruption of the Hunga volcano (Tonga) generated a rich spectrum of waves, some of which achieved global propagation. Among numerous platforms mon- itoring the event, two stratospheric balloons flying over the tropical Pacific provided unique observations of infrasonic wave arrivals, detecting five complete revolutions. Combined with ground measurements from the infrasound network of the International Monitor- ing System, balloon-borne observations may provide additional constraint on the scenario of the eruption, as suggested by the correlation between bursts of acoustic wave emis- sion and peaks of maximum volcanic plume top height. Balloon records also highlight previously unobserved long-range propagation of infrasound modes and their dispersion patterns. A comparison between ground- and balloon-based measurements emphasizes superior signal-to-noise ratios onboard the balloons and further demonstrates their po- tential for infrasound studies.

In 2007, the National Academies designated “understanding the structure & composition of the lunar interior” (to provide fundamental information on the evolution of a differentiated planetary body) as the second highest lunar science priority that needed to be addressed. Here we present the current status of the planned response of the Lunar Geophysical Network (LGN) team to the upcoming New Frontiers-5 AO. The Moon represents an end-member in the differentiation of rocky planetary bodies. Its small size (and heat budget) means that the early stages of differentiation have been frozen in time. But despite the success of the Apollo Lunar Surface Experiment Package (ALSEP), significant unresolved questions remain regarding the nature of the lunar interior and tectonic activity. General models of the processes that formed the present-day lunar interior are currently being challenged. While reinterpretation of the Apollo seismic data has led to the identification of a lunar core, it has also produced a thinning of the nearside lunar crust from 60-65 km in 1974 to 30-38 km today. With regard to the deep mantle, Apollo seismic data have been used to infer the presence of garnet below ~500 km, but the same data have also been used to identify Mg-rich olivine. A long-lived global lunar geophysical network (seismometer, heat flow probe, magnetometer, laser retro-reflector) is essential to defining the nature of the lunar interior and exploring the early stages of terrestrial planet evolution, add tremendous value to the GRAIL and SELENE gravity data, and allow other nodes to be added over time (ie, deliver the International Lunar Network). Identification of lateral and vertical heterogeneities, if present within the Moon, will yield important information about the early presence of a global lunar magma ocean (LMO) as well as exploring LMO cumulate overturn. LGN would also provide new constraints on seismicity, including shallow moonquakes (the largest type identified by ALSEP with magnitudes between 5-6) that have been linked to young thrust fault scarps, suggesting current tectonic activity. Advancing our understanding of the Moon’s interior is critical for addressing these and many other important lunar and Solar System science and exploration questions, including protection of astronauts from the strong shallow moonquakes.

Mars is the first extraterrestrial planet with seismometers (SEIS) deployed directly on its surface in the framework of the InSight (Interior Exploration using Seismic Investigations, Geodesy and Heat Transport) mission. The lack of strong Marsquakes, however, strengthens the need of seismic noise studies to additionally constrain the Martian structure. Seismic noise autocorrelations of single-station recordings permit the determination of the zero-offset reflection response underneath SEIS. We present a new autocorrelation study which employs state-of-the-art approaches to determine a robust reflection response by avoiding bias from aseismic signals which are recorded together with seismic waves due to unfavorable deployment and environmental conditions. Data selection and segmentation is performed in a data-adaptive manner which takes the data root-mean-square amplitude variability into account. We further use the amplitude-unbiased phase cross-correlation and work in the 1.2-8.9 Hz frequency band. The main target are crustal scale reflections, their robustness and convergence. The strongest signal appears at 10.6 s, and, if interpreted as P-wave reflection, would correspond to a discontinuity at about 24 km depth. This signal is a likely candidate for a reflection from the base of the Martian crust due to its strength, polarity, and stability. Additionally we identify, among the stable signals, a signal at about 6.85 s that can be interpreted as a P-wave reflection from the mid-crust at about 9.5 km depth.

The NASA InSight lander successfully placed a seismometer on the surface of Mars. Alongside, a hammering device was deployed that penetrated into the ground to attempt the first measurements of the planetary heat flow of Mars. The hammering of the heat probe generated repeated seismic signals that were registered by the seismometer and can potentially be used to image the shallow subsurface just below the lander. However, the broad frequency content of the seismic signals generated by the hammering extends beyond the Nyquist frequency governed by the seismometer's sampling rate of 100 samples per second. Here, we propose an algorithm to reconstruct the seismic signals beyond the classical sampling limits. We exploit the structure in the data due to thousands of repeated, only gradually varying hammering signals as the heat probe slowly penetrates into the ground. In addition, we make use of the fact that repeated hammering signals are sub-sampled differently due to the unsynchronised timing between the hammer strikes and the seismometer recordings. This allows us to reconstruct signals beyond the classical Nyquist frequency limit by enforcing a sparsity constraint on the signal in a modified Radon transform domain. Using both synthetic data and actual data recorded on Mars, we show how the proposed algorithm can be used to reconstruct the high-frequency hammering signal at very high resolution. In this way, we were able to constrain the seismic velocity of the top first meter of the Martian regolith.