We introduce the Seismic Language Model (SeisLM), a foundational model designed to analyze seismic waveforms -- signals generated by Earth's vibrations such as the ones originating from earthquakes. SeisLM is pretrained on a large collection of open-source seismic datasets using a self-supervised contrastive loss, akin to BERT in language modeling. This approach allows the model to learn general seismic waveform patterns from unlabeled data without being tied to specific downstream tasks. When fine-tuned, SeisLM excels in seismological tasks like event detection, phase-picking, onset time regression, and foreshock-aftershock classification. The code has been made publicly available on this https URL.

We present the first global-scale database of 4.3 billion P- and S-wave picks extracted from 1.3 PB continuous seismic data via a cloud-native workflow. Using cloud computing services on Amazon Web Services, we launched ~145,000 containerized jobs on continuous records from 47,354 stations spanning 2002-2025, completing in under three days. Phase arrivals were identified with a deep learning model, PhaseNet, through an open-source Python ecosystem for deep learning, SeisBench. To visualize and gain a global understanding of these picks, we present preliminary results about pick time series revealing Omori-law aftershock decay, seasonal variations linked to noise levels, and dense regional coverage that will enhance earthquake catalogs and machine-learning datasets. We provide all picks in a publicly queryable database, providing a powerful resource for researchers studying seismicity around the world. This report provides insights into the database and the underlying workflow, demonstrating the feasibility of petabyte-scale seismic data mining on the cloud and of providing intelligent data products to the community in an automated manner.

Dense ground displacement measurements are crucial for geological studies but are impractical to collect directly. Traditionally, displacement fields are estimated using patch matching on optical satellite images from different acquisition times. While deep learning-based optical flow models are promising, their adoption in ground deformation analysis is hindered by challenges such as the absence of real ground truth, the need for sub-pixel precision, and temporal variations due to geological or anthropogenic changes. In particular, we identify that deep learning models relying on explicit correlation layers struggle at estimating small displacements in real-world conditions. Instead, we propose a model that employs iterative refinements with explicit warping layers and a correlation-independent backbone, enabling sub-pixel precision. Additionally, a non-convex variant of Total Variation regularization preserves fault-line sharpness while maintaining smoothness elsewhere. Our model significantly outperforms widely used geophysics methods on semi-synthetic benchmarks and generalizes well to challenging real-world scenarios captured by both medium- and high-resolution sensors. Project page: this https URL

Reliable seismicity catalogs are essential for seismology. Following phase picking, phase association groups arrivals into sets with consistent origins (i.e., events), determines event counts, and identifies outlier picks. To handle the substantial increase in the quantity of seismic phase picks from improved picking methods and larger deployments, several novel phase associators have recently been proposed. This study presents a detailed benchmark analysis of five seismic phase associators, including classical and machine learning-based approaches: PhaseLink, REAL, GaMMA, GENIE, and PyOcto. We use synthetic datasets mimicking real seismicity characteristics in crustal and subduction zone scenarios. We evaluate performance for different conditions, including low- and high-noise environments, out-of-network events, very high event rates, and variable station density. The results reveal notable differences in precision, recall, and computational efficiency. GENIE and PyOcto demonstrate robust performance, with almost perfect performance for most scenarios. Only for the most challenging conditions with high noise levels and event rates, performance drops but still maintains F1 scores above 0.8. PhaseLink's performance declines with noise and event density, particularly in subduction zones, dropping to near zero in the most complex cases. GaMMA outperforms PhaseLink but struggles with accuracy and scalability in high-noise, high-density scenarios. REAL performs reasonably but loses recall under extreme conditions. PyOcto and PhaseLink show the quickest runtimes for smaller-scale datasets, while REAL and GENIE are more than an order of magnitude slower for these cases. At the highest pick rates, GENIE's runtime disadvantage diminishes, matching PyOcto and scaling effectively. Our results can guide practitioners compiling seismicity catalogs and developers designing novel associators.

Seismic waveforms contain rich information about earthquake processes, making effective data analysis crucial for earthquake monitoring, source characterization, and seismic hazard assessment. With rapid developments in deep learning, the state-of-the-art approach in artificial intelligence, many neural network models have been developed to enhance earthquake monitoring tasks, such as earthquake detection, phase picking, and phase association. However, most current efforts focus on developing separate models for each specific task, leaving the potential of an end-to-end framework relatively unexplored. To address this gap, we extend an existing phase picking model, PhaseNet, to create a multitask framework. This extended model, PhaseNet+, simultaneously performs phase arrival-time picking, first-motion polarity determination, and phase association. The outputs from these perception-based models can then be processed by specialized physics-based algorithms to accurately determine earthquake location and focal mechanism. The multitask approach is not limited to the PhaseNet model and can be applied to other state-of-the-art phase picking models, ultimately improving seismic monitoring through a more unified and efficient approach.

We invert for motions at the surface of Earth's core under spatial and temporal constraints that depart from the mathematical smoothings usually employed to ensure spectral convergence of the flow solutions. Our spatial constraints are derived from geodynamo simulations. The model is advected in time using stochastic differential equations coherent with the occurrence of geomagnetic jerks. Together with a Kalman filter, these spatial and temporal constraints enable the estimation of core flows as a function of length and time-scales. From synthetic experiments, we find it crucial to account for subgrid errors to obtain an unbiased reconstruction. This is achieved through an augmented state approach. We show that a significant contribution from diffusion to the geomagnetic secular variation should be considered even on short periods, because diffusion is dynamically related to the rapidly changing flow below the core surface. Our method, applied to geophysical observations over the period 1950-2015, gives access to reasonable solutions in terms of misfit to the data. We highlight an important signature of diffusion in the Eastern equatorial area, where the eccentric westward gyre reaches low latitudes, in relation with important up/down-wellings. Our results also confirm that the dipole decay, observed over the past decades, is primarily driven by advection processes. Our method allows us to provide probability densities for forecasts of the core flow and the secular variation.

Geological data show that, early in its history, the Earth had a large-scale magnetic field with an amplitude comparable to that of the present geomagnetic field. However, its origin remains enigmatic and various mechanisms have been proposed to explain the Earth's field over geological time scales. Here, we critically evaluate whether tidal forcing could explain the early Earth's geodynamo, by combining constraints from geophysical models of the Earth-Moon system and predictions from rotating turbulence studies. We show that the Moon's tides could be strong enough before $-3.25$ Gy to trigger turbulence in the Earth's core, and possibly dynamo action in the mean time. Then, we propose new scaling laws for the magnetic field amplitude $B$. We show that $B \propto \beta^{4/3}$, where $\beta$ is the typical equatorial ellipticity of the liquid core, if the turbulence involves weak interactions of three-dimensional inertial waves. Otherwise, we would expect $B \propto \beta$ if the amplitude of tidal forcing were strong enough. When extrapolated to the Earth's core, it suggests that tidal forcing alone was too weak to possibly explain the ancient paleomagnetic field. Therefore, our study indirectly favours another origin for the early Earth's geodynamo on long time scales (e.g. the exsolution of light elements atop the core, or thermal convection due to secular cooling).

The Atacama segment in Northern Chile (24°S to 31°S) is a mature seismic gap with no major event (Mw>8) since 1922. In addition to regular seismicity, around the subducting Copiapó ridge, the region hosts seismic swarms, and shallow and deep slow slip events. To characterize the fine structure of this seismic gap and its seismic-aseismic interplay, we instrumented the region with almost 200 seismic and geodetic stations. Using machine learning, we derived a dense, high-resolution seismicity catalog, encompassing over 165,000 events with double-difference relocated hypocenters. Our catalog details the outer rise, interface, intraslab, crustal and mantle wedge seismicity. We infer a detailed slab geometry, showing that the flat slab is dipping towards the south with a narrower extent along dip. The slab geometry controls the intraslab seismicity, with cross-cutting activity in the region of highest bending and a downdip limit around 105 km slab depth. Our catalogue exhibits significant seismicity in the mantle wedge upper corner between 28°S and 31°S, highlighting the brittle behavior of the cold nose. On the subduction interface, interplate locking controls the updip end of the seismicity, with seismicity extending closer to the trench in low-locking areas. On fine scales, resolved by relative uncertainties below 50 m, the subduction interface has a complex 3D structure, showing a fractal distribution of seismic patches down to a scale of tens of meters. Our results provide a holistic view of this complex subduction zone, while at the same time giving insights into fine-scale structures and processes.

We test the ability of large scale velocity fields inferred from geomagnetic secular variation data to produce the global magnetic field of the this http URL kinematic dynamo calculations use quasi-geostrophic (QG) flows inverted from geomagnetic field models which, as such, incorporate flow structures that are Earth-like and may be important for the this http URL, the QG hypothesis allows straightforward prolongation of the flow from the core surface to the this http URL expected from previous studies, we check that a simple quasi-geostrophic flow is not able to sustain the magnetic field against ohmic this http URL complexity is then introduced in the flow, inspired by the action of the Lorentz this http URL, on centenial time-scales, the Lorentz force can balance the Coriolis force and strict quasi-geostrophy may not be the best this http URL the columnar flow is modified to account for the action of the Lorentz force, magnetic field is generated for Elsasser numbers larger than 0.25 and magnetic Reynolds numbers larger than this http URL suggests that our large scale flow captures the relevant features for the generation of the Earth's magnetic field and that the invisible small scale flow may not be directly involved in this this http URL the threshold, the resulting magnetic field is dominated by an axial dipole, with some reversed flux this http URL-dependence is also considered, derived from principal component analysis applied to the inverted this http URL find that time periods from 120 to 50 years do not affect the mean growth rate of the kinematic this http URL we notice the footprint of the inner-core in the magnetic field generated deep in the bulk of the shell, although we did not include one in our computations.

Recent work in the field of cryo-seismology demonstrates that high frequency (>1 Hz) waves provide key constraints on a wide range of glacier processes such as basal friction, surface crevassing or subglacial water flow. Establishing quantitative links between the seismic signal and the processes of interest however requires detailed characterization of the wavefield, which at the high frequencies of interest necessitates the deployment of large and particularly dense seismic arrays. Although dense seismic array monitoring has recently become routine in geophysics, its application to glaciated environments has yet remained limited. Here we present a dense seismic array experiment made of 98 3-component seismic stations continuously recording during 35 days in early spring on the Argenti\`ere Glacier, French Alps. The seismic dataset is supplemented by a wide range of complementary observations obtained from ground penetrating radar, drone imagery, GNSS positioning and in-situ instrumentation of basal glacier sliding velocities and subglacial water discharge. Through applying multiple processing techniques including event detection from template matching and systematic beamforming, we demonstrate that the present dataset provides enhanced spatial resolution on basal stick slip and englacial fracturing sources as well as novel constraints on heterogeneous nature of the noise field generated by subglacial water flow and on the link between crevasse properties and englacial seismic velocities. We finally outline in which ways further work using this dataset could help tackle key remaining questions in the field.

Seismology has entered the petabyte era, driven by decades of continuous recordings of broadband networks, the increase in nodal seismic experiments, and the recent emergence of Distributed Acoustic Sensing (DAS). This review explains how commercial clouds - AWS, Google Cloud, and Azure - by providing object storage, elastic compute, and managed databases, enable researchers to "bring the code to the data," thereby overcoming traditional HPC solutions' bandwidth and capacity limitations. After literature reviews of cloud concepts and their research applications in seismology, we illustrate the capacities of cloud-native workflows using two canonical end-to-end demonstrations: 1) ambient noise seismology and cross-correlation, and 2) earthquake detection, discrimination, and phase picking. Both workflows utilized S3 for streaming I/O and DocumentDB for provenance, demonstrating that cloud throughput can rival on-premises HPC at comparable costs, scanning 100 TBs to 1.3 PBs of seismic data in a few hours or days of processing. The review also discusses research and education initiatives, the reproducibility benefits of containers, and cost pitfalls (e.g., egress, I/O fees) of energy-intensive seismological research computing. While designing cloud pipelines remains non-trivial, partnerships with research software engineers enable converting domain code into scalable, automated, and environmentally conscious solutions for next-generation seismology.

The crystallographic structure of iron under extreme conditions is a key benchmark for cutting-edge experimental and numerical methods. Moreover, it plays a crucial role in understanding planetary cores, as it significantly influences the interpretation of observational data and, consequently, insights into their internal structure and dynamics. However, even the structure of pure solid iron under the Earth's core conditions remains uncertain, with the commonly expected hexagonal close-packed structure energetically competitive with various cubic lattices. In this study, iron was compressed in a diamond anvil cell to above 200 GPa, and dynamically probed near the melting point using MHz frequency X-ray pulses from the European X-ray Free Electron Laser. The emergence of an additional diffraction line at high temperatures suggests the formation of an entropically stabilized bcc structure. Rapid heating and cooling cycles captured intermediate phases, offering new insights into iron's phase transformation paths. The appearance of the bcc phase near melting at extreme pressures challenges current understanding of the iron phase diagram under Earth's core conditions.

We consider the high-resolution seismic imaging method called full-waveform inversion (FWI). FWI is a data fitting method aimed at inverting for subsurface mechanical parameters. Despite the large adoption of FWI by the academic and industrial communities, and many successful results, FWI still suffers from severe limitations. From a mathematical standpoint, FWI is a large scale PDE-constrained optimization problem. The misfit function that is used, which measures the discrepancy between observed seismic data and data calculated through the solution of a wave propagation problem, is non-convex. After discretization, the size of the FWI problem requires the use of local optimization solvers, which are prone to converge towards local minima. Thus the success of FWI strongly depends on the choice of the initial model to ensure the convergence towards the global minimum of the misfit function. This limitation has been the motivation for a large variety of strategies. Among the different methods that have been investigated, the use of optimal transport (OT) distances-based misfit functions has been recently promoted. The leading idea is to benefit from the inherent convexity of OT distances with respect to dilation and translation to render the FWI problem more convex. However, the application of OT distances in the framework of FWI is not straightforward, as seismic data is signed, while OT has been developed for the comparison of probability measures. The purpose of this study is to review two methods that were developed to overcome this difficulty. Both have been successfully applied to field data in an industrial framework. Both make it possible to better exploit the seismic data, alleviating the sensitivity to the initial model and to various conventional workflow steps, and reducing the uncertainty attached to the subsurface mechanical parameters inversion.

The ubiquitous phenomena of crystallization and melting occur in various geophysical contexts across many spatial and temporal scales. In particular, they take place in the iron core of terrestrial planets and moons, profoundly influencing their dynamics and magnetic field generation. Crystallization and melting entail intricate multiphase flows, buoyancy effects, and out-of-equilibrium thermodynamics, posing challenges for theoretical modeling and numerical simulations. Besides, due to the inaccessible nature of the planetary deep interior, our understanding relies on indirect data from seismology, mineral physics, geochemistry, and magnetism. Consequently, phase-change-driven flows in planetary cores constitute a compelling yet challenging area of research. This paper provides an overview of the role of laboratory fluid dynamics experiments in elucidating the solid-liquid phase change phenomena occurring thousands of kilometers beneath our feet and within other planetary depths, along with their dynamic repercussions. Drawing parallel with metallurgy, it navigates through all scales of phase change dynamics, from microscopic processes (nucleation and crystal growth) to macroscopic consequences (solid-liquid segregation and large-scale flows). The review delves into the two primary planetary solidification regimes, top-down and bottom-up, and elucidates the formation of mushy and/or slurry layers in the various relevant configurations. Additionally, it outlines remaining challenges, including insights from ongoing space missions poised to unveil the diverse planetary regimes.

Laboratory spectral measurements of relevant analogue materials were performed in the framework of the Rosetta mission in order to explain the surface spectral properties of comet 67P. Fine powders of coal, iron sulphides, silicates and their mixtures were prepared and their spectra measured in the Vis-IR range. These spectra are compared to a reference spectrum of 67P nucleus obtained with the VIRTIS/Rosetta instrument up to 2.7 {\mu}m, excluding the organics band centred at 3.2 {\mu}m. The species used are known to be chemical analogues for cometary materials which could be present at the surface of 67P. Grain sizes of the powders range from tens of nanometres to hundreds of micrometres. Some of the mixtures studied here actually reach the very low reflectance level observed by VIRTIS on 67P. The best match is provided by a mixture of sub-micron coal, pyrrhotite, and silicates. Grain sizes are in agreement with the sizes of the dust particles detected by the GIADA, MIDAS and COSIMA instruments on board Rosetta. The coal used in the experiment is responsible for the spectral slope in the visible and infrared ranges. Pyrrhotite, which is strongly absorbing, is responsible for the low albedo observed in the NIR. The darkest components dominate the spectra, especially within intimate mixtures. Depending on sample preparation, pyrrhotite can coat the coal and silicate aggregates. Such coating effects can affect the spectra as much as particle size. In contrast, silicates seem to play a minor role.

Subduction megathrusts release stress not only seismically through earthquakes, but also through creep and transient slow deformation, called slow slip events (SSEs). Understanding the interplay between fast and slow slip is essential for illuminating the deformation processes on the subduction interface. The Chilean subduction margin, while one of the most seismically active regions worldwide, has few reports of SSEs. Furthermore, there are no comprehensive reports of tectonic tremors or low-frequency earthquakes (LFEs), seismic signals typically accompanying SSEs, tracking deformation at small spatial and temporal scales. Here, we perform a systematic search for tectonic tremors and LFEs in the Atacama segment in Northern Chile, a region hosting both shallow and deep SSEs. Using dense seismic networks, we investigate 3.5 years between November 2020 and February 2024. Due to the network geometry, we focus on deep tremor and LFEs. We apply two orthogonal methods, envelope correlation for tremor search and deep learning detection for LFEs, to generate initial catalogs. To validate the potential detections, we use clustering, matched filtering, heuristics, and extensive manual inspection. While our initial search provides numerous candidates, after verification, we find no evidence for tectonic tremor or LFEs in the region. In contrast, our approaches successfully recover tremors and LFEs in two reference regions outside Chile with known tremor and LFE activity. Our observations show that tremors and LFEs in Northern Chile are either of lower moment rate than in other regions, have substantially longer recurrence rates, or are absent altogether, potentially due to the cold subduction.

Considering the purpose of the session relating early engineering developments in site response and soil-structure interaction, this paper focuses on the development of studies regarding site-city interaction following the striking site response observations obtained in Mexico City during the 1985 Guerrero-Michoacan event, The first part presents an overview of the investigations on multiple structure-soil-structure interaction, starting with Mexico-city like environments with dense urbanization on soft soils, which later evolved with the concept of metamaterials. Up to now, such investigations have been largely relying on numerical simulations in 2D and 3D media, coupling soft surface soil layers and simplified building models, including also some theoretical developments using various mechanical concepts. They also relied on a number of laboratory experiments on reduced-scale mock-ups with diverse vibratory sources (shaking table, acoustic devices). The latest studies coupled full-scale experiments on mechanical analogs such as forests or wind turbine farms involving sets of resonators with similar frequencies, and numerical simulation to investigate their impact on the propagation of surface (Rayleigh) waves. Almost all such studies converge in predicting lower ground motion amplitude for sites located within the ''urbanized'' area, but none of them can be considered a ''groundtruth'' proof for a real earthquake in a real city. The second part thus takes advantage of the long duration of strong motion observations in the Kanto area thanks to the KiK-net, K-NET and JMA (Shin-dokei) networks, to investigate the possible changes in site response with time. The first results obtained with the event-specific site terms derived from Generalized Inversion Techniques (Nakano et al., 2015) indicate a systematic reduction of the low frequency (0.2 -1 Hz) site amplification, in the central-south Tokyo area. As this frequency band corresponds both to the site frequency (very thick deposits) and to the high-rise buildings, the discussion focuses on the possible relation with the extensive construction in some areas of downtown Tokyo over the last 2 decades.

Using discrete element simulations based on molecular dynamics, we investigate the mechanical behavior of sheared, dry, frictional granular media in the "dense" and "critical" regimes. We find that this behavior is partitioned between transient stages and a final stationary stage. While the later is macroscopically consistent with the predictions of the viscous, $\mu(I)$ rheology, both the macroscopic behavior during the transient stages and the overall microscopic behavior suggest a more complex picture. Indeed, the simulated granular medium exhibits a finite elastic stiffness throughout its entire shear deformation history, although topological rearrangements of the grains at the microscale translate into a partial degradation of this stiffness, which can be interpreted as a form of elastic damage. The relaxation of stresses follows a compressed exponential, also highlighting the role of elastic interactions in the medium, with residual stresses that depend on the level of elastic damage. The established relations between elastic and relaxation properties point to a complex rheology, characterized by a damage-dependent transition between a visco-elasto-plastic and a viscous behavior.

Stably-stratified fluid layers are ubiquitous in gaseous planets, stellar and liquid cores, and have previously been thought not to be capable of driving a dynamo. We demonstrate that semiconvection (convection driven by a destabilising thermal gradient in an overall stably stratified region) can actually lead to dynamo action in such regions. Motivated by recent works suggesting that significant regions of Jupiter and Saturn may be prone to semiconvection, we conduct direct numerical simulations in spherical shells in the planetary relevant limit of low magnetic Prandtl numbers. We retrieve key characteristics of planetary magnetic fields, including dipolarity, magnetic field strength, and the form of the energy spectrum. This short demonstration opens the possibility for further more detailed studies of semiconvection dynamos, that can also be relevant for stellar interiors.

Stressed under a constant load, materials creep with a final acceleration of deformation and for any given applied stress and material, the creep failure time can strongly vary. We investigate creep on sheets of paper and confront the statistics with a simple fiber bundle model of creep failure in a disordered landscape. In the experiments, acoustic emission event times $t_j$ were recorded, and both this data and simulation event series reveal sample-dependent history effects with log-normal statistics and non-Markovian behavior. This leads to a relationship between $t_j$ and the failure time $t_f$ with a power law relationship, evolving with time. These effects and the predictability result from how the energy gap distribution develops during creep.