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.

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

Characterizing the large M4.7+ seismic events during the 2018 Kīlauea eruption is important to understand the complex subsurface deformation at the Kīlauea summit. The first 12 events (May 17 - May 26) are associated with long-duration seismic signals and the remaining 50 events (May 29 - August 02) are accompanied by large-scale caldera collapses. Resolving the source location and mechanism is challenging because of the shallow source depth, significant non double-couple components, and complex velocity structure. We demonstrate that combining multiple geophysical data from broadband seismometers, accelerometers and infrasound is essential to resolve different aspects of the seismic source. Seismic moment tensor solutions using near-field summit stations show the early events are highly volumetric. Infrasound data and particle motion analysis identify the inflation source as the Halema’uma’u reservoir. For the later collapse events, two independent moment tensor inversions using local and global stations consistently show that asymmetric slips occur on inward-dipping normal faults along the northwest corner of the caldera. While the source mechanism from May 29 onwards is not fully resolvable seismically using far-field stations, infrasound records and simulations suggest there may be inflation during the collapse. The summit events are characterized by both inflation and asymmetric slip, which are consistent with geodetic data. Based on the location of the slip and microseismicity, the caldera may have failed in a ‘see-saw’ manner: small continuous slips in the form of microseismicity on the southeast corner of the caldera, compensated by large slips on the northwest during the large collapse events.

Modelling the spatial distribution of infrasound attenuation (or transmission loss, TL) is key to understanding and interpreting microbarometer data and observations. Such predictions enable the reliable assessment of infrasound source characteristics such as ground pressure levels associated with earthquakes, man-made or volcanic explosion properties, and ocean-generated microbarom wavefields. However, the computational cost inherent in full-waveform modelling tools, such as Parabolic Equation (PE) codes, often prevents the exploration of a large parameter space, i.e., variations in wind models, source frequency, and source location, when deriving reliable estimates of source or atmospheric properties – in particular for real-time and near-real-time applications. Therefore, many studies rely on analytical regression-based heuristic TL equations that neglect complex vertical wind variations and the range-dependent variation in the atmospheric properties. This introduces significant uncertainties in the predicted TL. In the current contribution, we propose a deep learning approach trained on a large set of simulated wavefields generated using PE simulations and realistic atmospheric winds to predict infrasound ground-level amplitudes up to 1000 km from a ground-based source. Realistic range dependent atmospheric winds are constructed by combining ERA5, NRLMSISE-00, and HWM-14 atmospheric models, and small-scale gravity-wave perturbations computed using the Gardner model. Given a set of wind profiles as input, our new modelling framework provides a fast (0.05 s runtime) and reliable (~5 dB error on average, compared to PE simulations) estimate of the infrasound TL.

Tsunamis generated by large earthquake-induced displacements of the ocean floor can lead to tragic consequences for coastal communities. Ionospheric measurements of Co-Seismic Disturbances (CIDs) offer a unique solution to characterize an earthquake’s tsunami potential in Near-Real-Time (NRT) since CIDs can be detected within 15 min of a seismic event. However, the detection of CIDs relies on human experts, which currently prevents the deployment of ionospheric methods in NRT. To address this critical lack of automatic procedure, we designed a machine-learning based framework to (1) classify ionospheric waveforms into CIDs and noise, (2) pick CID arrival times, and (3) associate arrivals across a satellite network in NRT. Machine-learning models (random forests) trained over an extensive ionospheric waveform dataset show excellent classification and arrival-time picking performances compared to existing detection procedures, which paves the way for the NRT imaging of surface displacements from the ionosphere.

Deploying seismic or infrasound arrays on the ground to probe a planet’s interior structure remains challenging in remote regions facing harsh surface conditions such as Venus with a surface temperature of 464°C. Fortunately, a fraction of the seismic energy transmits in the upper atmosphere as infrasound waves, i.e. low-frequency pressure perturbations (< 20Hz). On July 22, 2019, a heliotrope balloon, equipped with pressure sensors, was launched from the Johnson Valley, CA with the objective of capturing infrasound signals from the aftershock sequence of the 2019 Ridgecrest earthquake. At 16:27:36 UTC, the sound of a natural earthquake of Mw 4.2 was detected for the first time by a balloon platform. This observation offered the opportunity to attempt the first inversion of seismic velocities from the atmosphere. Shear velocities extracted by our analytical inversion method fell within a reasonable range from the values provided by regional tomographic models. While our analysis was limited by the observation’s low signal-to-noise ratio, future observations of seismic events from a network of balloons carrying multiple pressure sensors could provide excellent constraints on crustal properties. However, to build robust estimates of seismic properties, inversion procedures will have to account for uncertainties in terms of velocity models, source locations, and instrumental errors. In this contribution, we will discuss the current state of balloon-based observations, the sensitivity of the acoustic wavefield on subsurface properties, and perspectives on future inversions of seismically-induced acoustic data.

Sedimentary basins strongly affect earthquake ground motions of both body and surface waves that propagate through them. Yet to characterize seismic hazards at a specific site, it is common practice to consider only the effects of near-surface geology on vertically propagating body waves despite surface waves often causing strong damage. Recently, Bowden & Tsai (2017) proposed an semi-analytical method to predict surface-wave basin amplification and noticed that certain large regional earthquake ground motions are under-predicted if surface waves are not properly accounted for. Since the theory is based on a 1-D approximation of the near-surface geologic structure and does not account for path effects, it is of interest to know how significantly such additional complexity affects the 1-D predictions. When considering deep basins, several other basin parameters play a role in the amplification of surface waves: transmission and conversion at the basin edge, basin shape, lateral resonance and focusing effects. As surface waves propagate back and forth in a highly dispersive medium, the amplification also varies strongly from the edge to the center of the basin. These effects are not always accounted for because of the cost of geophysical surveys that would accurately constrain the structure, the lack of earthquake data for empirical predictions, the poor understanding of what main factors are responsible for basin amplification, and the absence of quantitative estimates of their contribution to the overall amplification. The current study aims to provide quantitative estimates of the importance of these various path effects on surface waves amplification and also extend the current 1-D theory to more complex multi-dimensional basin structures.

The mechanical coupling between a planet and its atmosphere enables the conversion of seismic waves into infrasound waves, i.e. low-frequency pressure perturbations (< 20Hz), which propagate to the upper atmosphere. Since the characteristics of the seismically-induced pressure perturbations are connected to their seismic counterparts, they provide a unique opportunity to investigate the atmospheric and interior structures of a planet or to constrain source properties. However, in Earth’s remote regions, deploying seismic or infrasound networks at the surface can be a difficult task. Stratospheric balloon platforms equipped with pressure sensors have therefore gained interest since they provide a unique and inexpensive way to record pressure signals in the atmosphere with a low noise level. Yet, infrasound observations of Earthquakes on balloon platforms have never been reported in the literature. In this study, we investigate the seismo-acoustic wavefield generated by the aftershocks of the 2019 Ridgecrest sequence and other regional low-magnitude Earthquakes on July 22 and August 9, 2019 using four free-flying balloons equipped with pressure sensors. We observed a strong signal coherence after the largest event between seismic motions at the surface and balloon pressure variations which matches our numerical simulations. A first atmospheric earthquake detection is crucial to demonstrate the viability of this novel technique to monitor infrasound from natural and artificial seismicity on Earth, and the study of seismic activity on planets such as Venus.