Sven Peter Näsholm

On 19 February 2025, a SpaceX Falcon 9 rocket stage re-entered the atmosphere over Ireland’s west coast. It produced a sustained fireball along its trajectory across the United Kingdom, the Netherlands, northern Germany, and western Poland. Seismic and infrasound stations in the Netherlands and Germany, as well as infrasound arrays in Sweden and Norway, recorded the event. We characterise the re-entry as a line source and reproduce the trajectory using the seismoacoustic observations of more than 80 stations. The majority of the stations detected two distinct signal arrivals. We investigate the detection pattern with varying time intervals between these signals across the station networks, benefiting from both a dense seismic borehole network in the Netherlands and co-located measurements of infrasound and seismic stations. Acoustic arrivals at stations up to 150 km distance from the trajectory originate from the shock wave of the Mach cone due to the hypersonic velocity of the re-entering debris. Infrasound arrays at distances between 400 and 900 km additionally recorded signals that also reflect the west-to-east movement of the source. Allsky7 camera observations and infrasound propagation simulations utilising the UA-ICON model atmospheric representation enable the validation of the seismoacoustic characterisation of the re-entry. Co-located atmospheric lidar measurements provide valuable insight into small-scale atmospheric dynamics influencing infrasound propagation conditions. This study demonstrates that dense seismoacoustic station networks, including infrasound arrays, can provide valuable ground-based and time-constrained evidence of re-entering space debris over a wide horizontal extent. In an era of an increasing number of rocket launches and deorbiting space debris, these seismoacoustic observations can also be relevant for space agencies to re-analyse such events.

Reliable conflict data are critical for peace keeping missions. Yet, openly-available satellite data are costly and often have limited temporal resolution, while social media and news reports can be highly biased, time-consuming to verify and may place human lives at risk. Dando et al. (2023) showed that regional seismic networks can constrain the location and timing of conflict-related explosions with an unprecedented level of spatial and temporal resolution. However, such a dataset was built using STA/LTA characteristic functions, which cannot easily discriminate between seismic phases nor identify acoustic arrivals in seismic records. This limits event detection capability and increases uncertainty in the derived source parameters. Here, we develop a deep-learning phase picker which exploits waveform features through a U-net architecture and network geometry through a graph convolution module to discriminate between seismic (P and S) and acoustic (IS) phases. The model is trained on manually labeled seismic and acoustic picks from conflict events recorded at the Malyn array in Ukraine during 2022. By applying migration stacking to the model's phase detection probabilities, we detect 54% more seismic events than in Dando et al. (2023), and 557 previously-unidentified purely acoustic events. Our joint seismic and acoustic event catalog paves the way for future waveform analyses for peace-keeping operations, to discriminate between weapon types, or better constrain conflict dynamics.

Ultrasound time-harmonic elastography (THE) estimates tissue mechanical properties by imaging continuously driven shear waves. Systematic comparison and validation of ultrasound THE methods are limited by the lack of controllable ultrasound data with known shear-wave motion and material properties. In this work, we present a simulation framework for generating synthetic ultrasound RF channel data for THE. Numerically simulated shear-wave motion is converted into time-varying scatterer distributions, which are used in pulse-echo ultrasound simulations to produce speckle-consistent data. This moving-scatterer representation provides an explicit link between shear-wave propagation modeling and ultrasound image formation while retaining access to the underlying ground-truth motion field. The framework is demonstrated using a phantom-mimicking geometry and a realistic three-dimensional liver model. The generated channel data are processed using conventional beamforming, autocorrelation-based particle-velocity estimation and k-MDEV shear-wave speed reconstruction to illustrate their use in downstream THE analysis. The examples illustrate experimentally relevant effects associated with imaging orientation, boundary reflections and ultrasound attenuation. The proposed framework provides a controlled and reproducible source of synthetic ultrasound data for developing and evaluating ultrasound acquisition, beamforming and motion-estimation methods in THE.

The prevalence of explosive volcanism on Venus is poorly constrained due to a lack of in-situ and remote sensing data. Balloon‑borne pressure sensors in the middle atmosphere have the potential to detect infrasound from eruptions and thereby probe volcanic activity. In the current work, infrasound propagation is modeled using global wind and temperature fields from the Venus Climate Database, combined with an extension to the ray-based tau-p approach, as well as transmission loss simulations. We discover that strong zonal superrotation and subsolar‑to‑antisolar circulation generate persistent acoustic waveguides capable of channeling atmospheric infrasound in both zonal and meridional directions. To assess volcanic detectability, we couple global transmission loss estimates to a source model, relating Volcanic Explosivity Index to erupted mass, mass-flux histories, source pressure spectra, and Venus eruption statistics. Our statistical framework produces probability estimates of detecting at least one volcano for any target Signal-to-Noise Ratio for different balloon trajectories and mission durations. The results indicate that, for realistic noise levels, 6-month long balloon missions at ~60 km altitude can achieve up to 80% detection probability if only high-altitude volcanoes are considered. The detection rate varies with eruption duration, which drives the eruption pressure in our model. When considering long-duration eruptions, the detection probability drops below 30%, but it can reach 99% if all large volcanoes are considered active. The findings demonstrate that future long‑duration balloon missions equipped with infrasound pressure sensors have great potential to provide novel data to constrain the rate and spatial distribution of explosive volcanism on Venus.

Objective. In ultrasound time-harmonic elastography (THE), tissue elasticity is estimated from the ultrasound-measured shear-wave displacement fields, generated by an external vibrator. Commonly used techniques to map from the ultrasound pulse-echo data to tissue elasticity maps, such as the phase-gradient and local-frequency estimation methods, base their estimates on a set of displacement field data and do not involve direct numerical modeling of the underlying shear-wave propagation. In the current proof-of-concept study, the objective is to develop a full-waveform inversion (FWI) approach to ultrasound-based THE, providing a physics-driven framework for improved tissue elasticity estimation. Approach. FWI is a methodology commonly applied in geophysics. It relies on simulating the wave propagation and scattering in heterogeneous media to model the intricate relationship between the observed wavefield and the propagation medium properties. Our FWI-based tissue-harmonic elastography approach is based on minimizing a cross-correlation based objective function that quantifies phase mismatches between measured and simulated shear waves. Main Results. Experiments on synthetic phantom data obtained from the fractional Kelvin–Voigt wave model demonstrate that our approach accurately reconstructs the shear wave speed of the medium, even in the presence of significant noise, and without relying on pre-filtering steps such as directional filtering, which selects waves based on their propagation direction. An improvement of approximately 0.4 m⋅s ⁻¹ within the inclusion region, compared to an established method, is observed for a noisy wavefield with decreasing signal-to-noise ratio, ranging from 20 to −10 dB from top to bottom of the ultrasound image. In addition, the reconstructed shear-wave speed map achieved a 50% reduction in standard deviation. Significance. This work demonstrates the potential for enhanced elasticity mapping using FWI-based reconstruction in THE with prospective impacts for future clinical applications.

Venus' evolution remains a mystery because of the lack of in-situ geophysical data to constrain its interior structure. Recently-selected planetary missions VERITAS (NASA), DAVINCI+ (NASA), and EnVision (ESA) will investigate the planet's interior, surface, and atmospheric chemistry. However, none of these missions includes sensors capable of accurately probing Venus' crustal and mantle properties. Seismometer deployments are challenging on Venus due to high surface temperature and pressure. Acoustic balloon measurements and airglow observations -- that monitor Venus' upper atmosphere glow caused by chemical and radiative processes -- have been suggested as alternatives to surface deployments. However, it is critical to assess the potential of such missions under realistic conditions of geology, atmospheric states, network geometry, and seismicity using physics-based modeling. We employ a probabilistic framework to investigate detection probabilities as a function of Signal-to-Noise Ratio (SNR) for airglow and acoustic balloon missions using wave simulations, thermodynamically-consistent seismic velocity models, and realistic seismicity estimates. Our results demonstrate that the probability of detecting a single venusquake at SNR>1 over 6 months is around 65% across an entire 3-balloon network of about 5000km extent. Probabilities using dayglow imager data are below 60% and below 10% using nightglow data. Seismo-volcanic sequences enhance detectability but only if high seismic activity occurs at multiple volcanoes. Long-duration missions with both airglow and balloon-borne sensors could allow seismic wave measurements over a broad range of frequencies. Our results are highly dependent on seismic velocities, attenuation, seismicity, noise levels, mission duration, and airglow-coupling efficiency which should be the focus of future studies.

The conceptual clarity of any scientific field depends fundamentally on the precision and standardisation of its terminology. Prior studies have shown that an absence of standardized terminologies can lead to interpretive ambiguity, imprecise outputs, and divergent interpretations across research communities. In seismology, terminologies remain scattered across institutional glossaries, impeding data FAIRness (Findability, Accessibility, Interoperability, and Reusability), metadata consistency, and collaboration with adjacent fields such as transdisciplinary research and AI engineering.This work, carried out within the Geo-INQUIRE* project, introduces a vocabulary generation framework and a prototype database implementing three integrated innovations that consolidate the sparse seismological terminologies into a structured, machine-readable format: i) authority-first retrieval, ii) AI-mediated semantic triangulation, and iii) participatory expert governance.The authority-first pathway performs weighted, priority-ranked extraction from eight expert-curated data centre sources (including FDSN, USGS, EarthScope, EPOS, and other relevant documents from the community), ensuring that the definitions originate from trusted references. The AI fallback pathway is activated only when authoritative retrieval fails, employing a semantic triangulation method in which three large language models - such as OpenAI's GPT-5.2, Anthropic's Claude Opus 4.5, and Google's Gemini 3 - independently generate candidate definitions. Embedding-based similarity analysis determines synthesis eligibility; if cross-model agreement falls below 50 percent, an expert flag is raised to prevent semantic uncertainty. When synthesis proceeds, a transparent concept-merging process extracts common and unique contributions from each model, recording all reasoning steps and preserving full provenance, overcoming a critical limitation of black-box AI knowledge generation.Beyond technical generation, this work embeds vocabulary development within a participatory framework that transforms terminology from static definitions into community-validated knowledge. Through structured digital deliberation involving more than ten domain experts via a GitHub-based workflow, the approach delivers transparency, auditability, and collective ownership. Experts validate AI-retrieved content, resolve edge cases, and steward terminology evolution through documented discussion threads, ensuring definitions reflect both institutional authority and practitioner consensus while fostering public trust in seismology.The system produces vocabulary encoding scheme-compliant entries with dual definitions: an authoritative version weighted by source priority, and an AI-synthesized alternative with full provenance. The source-weighting mechanism is fully flexible ensuring the reusability of the framework. Applied to over 500 terms across 4 thematic clusters, this framework demonstrates that AI can systematically extend vocabulary completeness while participatory governance safeguards epistemic integrity. By coupling algorithmic precision with community oversight, this framework strengthens data discovery, metadata coherence, and research infrastructure interoperability across European and international seismological networks that advance transparent, reproducible, and interoperable seismological science.*Geo-INQUIRE (Geosphere INfrastructures for QUestions into Integrated REsearch) is funded by the European Union (GA 101058518).