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.

Utilizing existing telecommunication cables for Distributed Acoustic Sensing (DAS) experiments has eased the collection of seismological data in previously difficult-to-access areas such as the ocean bottom. To assess the potential of submarine DAS for monitoring seismic activity, we conducted an experiment from mid-October to mid-December 2021 using a 45 km long dark fiber extending from the Greek island of Santorini along the ocean bottom to the neighboring island of Ios. This region is of great geophysical and public interest because of its historical and recent seismic and volcanic activity, especially along the Kolumbo volcanic chain. Besides recording anthropogenic noise and around 1000 seismic events, we observe the primary and secondary microseisms in the submarine section, the latter inducing Scholte waves in a sediment layer where the cable is well-coupled. By using the spectral element wave propagation solver Salvus, we compute synthetic strains for earthquakes with varying degrees of model complexity. Despite including topography, a water layer, and a heterogeneous velocity model, we are unable to reproduce the lack of coherence in our observed earthquake waveforms. Backpropagation simulations for four observed earthquakes indicate that clear convergence of the wavefield, and thus the ability to constrain a source region, is only possible when all model complexities are considered. We conclude that, despite the promising emergence of DAS, monitoring capabilities are limited by often unfavorable cable geometries, cable coupling, and the complexity of the medium. Interrogating multiple cables simultaneously or jointly analyzing DAS and seismometer data could help improve future monitoring experiments.

We demonstrate the logistic feasibility and scientific potential of Distributed Acoustic Sensing (DAS) in alpine volcano-glacial environments that are subject to a broad range of natural hazards. Our work considers the Mount Meager massif, an active volcanic complex in British Columbia, estimated to have the largest geothermal potential in Canada, and home of Canada’s largest recorded landslide in 2010. From September to October 2019, we acquired continuous strain data, using a 3 km long fiber-optic cable, deployed on a ridge of Mount Meager and on the uppermost part of a glacier above 2000 m altitude. The data analysis detected a broad range of unexpectedly intense, low-magnitude, local seismicity. The most prominent events include long-lasting, intermediate-frequency (0.01 - 1 Hz) tremor, and high-frequency (5 - 45 Hz) earthquakes that form distinct spatial clusters and often repeat with nearly identical waveforms. We conservatively estimate that the number of detectable high-frequency events varied between several tens and nearly 400 per day. We also develop a beamforming algorithm that uses the signal-to-noise ratio (SNR) of individual channels, and implicitly takes the direction-dependent sensitivity of DAS into account. Both the tremor and the high-frequency earthquakes are most likely related to fluid movement within Mount Meager’s geothermal reservoir. Our work illustrates that DAS carries the potential to reveal previously undiscovered seismicity in challenging environments, where comparably dense arrays of conventional seismometers are difficult to install. We hope that the logistics and deployment details provided here may serve as a starting point for future DAS experiments.

Seismologists working with fiber-optic sensing, commonly referred to as Distributed Acoustic Sensing (DAS), have yet to find an established way of automatically detecting signals of interest within its recordings. We propose a new research perspective within the field by examining the output of a DAS array as an image and processing the image to find signals of interest. In this manuscript, we show an example of such a method, where we automatically detect seismic events of interest within two different DAS datasets, finding, respectively 99 % and 96 % of the local earthquakes previously identified within the data by manual analysis. The method is based on simple image processing and computer vision techniques, which clean the image, and, ideally, leave nothing but the signal of interest. These simple image processing steps yield promising results, indicating that computer vision and image processing might have an immediate impact in geophysical applications of fiber-optic sensing.