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AbstractTransient stress changes from seismic waves of distant earthquakes can promote local fault slip, a phenomenon referred to as remote dynamic triggering. This study examines the remote triggering susceptibility of faults in the Lower Rhine Embayment (LRE) in the Weisweiler region, Germany, a proposed site for geothermal energy production. Assessment of remote triggering can guide industrial operations to assess seismic hazard and mitigate risks associated with fault reactivation caused by small stress perturbations. We select a set of 23 candidate mainshocks from global earthquake catalogs that produce peak ground velocities (PGVs) that exceed 0.02 cm/s in the LRE. The magnitude of these mainshocks ranges from 5.4 to 9.1, epicentral distances range from 50 to 12,300 km, and back azimuth ranges from 16○ to 350○ with a maximum azimuthal gap of 91○. The candidate mainshocks generated PGVs locally from 0.02 to 0.28 cm/s (compared to typical threshold values ranging from 0.02 to 6 cm/s), corresponding to dynamic stress (σpd) values of 1.4 to 26 kPa. We use P-statistics and waveform data from local seismic stations to identify seismicity rate changes and uncatalogued earthquakes that were potentially triggered by the passing mainshock waves. The analysis reveals a statistically significant increase in seismicity rates following four mainshocks: the 1992 Mw5.4 Roermond, Netherlands, 2021 Mw8.2 Chignik, Alsaka, USA, 2023 Mw7.6 Kahramanmaraş, Republic of Türkiye, and 2025 Mw8.8 Kamchatka, Russia earthquakes. The 1992 Roermond mainshock triggered earthquakes within 50 km of its epicenter that were clustered between the Feldbiss and Sandgewand faults. The same area experienced a triggered earthquake sequence following the Chignik mainshock, suggesting that future detailed monitoring in this area may be warranted. The Roermond aftershock distribution can be divided two groups of events, including 61 that occur on the fault and in the near-field, which can be explained by static-stress increase and fluid diffusion. Another 32 remote aftershocks occurred that are consistent with secondary triggering promoted by aseismic slip propagation. The alignment of triggering mainshock back azimuths with the dominant strike direction of regional faults suggests that the orientation of incoming seismic waves is an important factor influencing susceptibility. Despite evidence of triggering, the majority of mainshocks (19 out of 23) were not followed by detectable seismicity-rate changes in the LRE, highlighting the complexity of conditions that lead to remote dynamic triggering. The study area does not respond to a triggering stress threshold, suggesting that non-linear, or a combination of linear and non-linear effects, dominate possible triggering mechanisms. Although the LRE does not respond to a clear triggering threshold, this study demonstrates that peak dynamic stress perturbations of approximately 1.4 kPa or greater can still trigger earthquakes. But, susceptibility is modulated by additional factors such as fault orientation, earthquake fault-zone properties, their state in the seismic cycle, and pre-existing stress state.

AbstractPhysics-informed neural networks (PINNs) face significant challenges in modeling multi-frequency wavefields in complex velocity models due to their slow convergence, difficulty in representing high-frequency details, and lack of generalization to varying frequencies and velocity scenarios. To address these issues, we propose Meta-LRPINN, a novel framework that combines low-rank parameterization using singular value decomposition (SVD) with meta-learning and frequency embedding. Specifically, we decompose the weights of PINN’s hidden layers using SVD and introduce an innovative frequency embedding hypernetwork (FEH) that links input frequencies with the singular values, enabling efficient and frequency-adaptive wavefield representation. Meta-learning is employed to provide robust initialization, improving optimization stability and reducing training time. Additionally, we implement adaptive rank reduction and FEH pruning during the meta-testing phase to further enhance efficiency. Numerical experiments, which are presented on multi-frequency scattered wavefields for different velocity models, demonstrate that Meta-LRPINN achieves much faster convergence speed and much higher accuracy compared to baseline methods such as Meta-PINN and vanilla PINN. Also, the proposed framework shows strong generalization to out-of-distribution frequencies while maintaining computational efficiency. These results highlight the potential of our Meta-LRPINN for scalable and adaptable seismic wavefield modeling.

AbstractP-wave reflections from the 410- and 660-km mantle discontinuities are visible in stacks of ambient noise cross-correlation functions of USArray stations spanning the contiguous United States. The reflections are most visible on the vertical components at frequencies between 0.1 and 0.3 Hz during low-noise periods, which generally occur during the summer months in the northern hemisphere. Common reflection point stacking can be used to resolve apparent lateral differences in discontinuity structure across the continent and suggests the possible existence of sporadic reflectors at other depths. Visibility of the 660-km reflector is correlated with faster P-wave velocities at similar depth in a tomographic model for North America. However, the lack of clear agreement between these P-wave ambient noise features and prior mantle-transition-zone imaging studies using other methods suggests caution should be applied in their interpretation. Ambient noise sources from the southern oceans may not be distributed uniformly enough for cross-correlation stacks to provide unbiased estimates of the true station-to-station P-wave Green’s functions. However, the clear presence of 410- and 660-km reflections in the ambient noise data suggests that it should be possible to unravel the complexities associated with varying noise source locations to produce reliable P-wave reflection profiles, providing new insights into mantle structure under the contiguous United States.

AbstractSeismic wave propagation in poro-viscoelastic anisotropic media is of practical importance for exploration geophysics and global seismology. Existing theories generally utilize homogeneous plane wave theory, which considers only velocity anisotropy but neglects attenuation anisotropy and wave inhomogeneity arising from attenuation. As a result, it poses significant challenges to accurately analyze seismic wave dispersion and attenuation in poro-viscoelastic anisotropic media. In this paper, we investigate the propagation of inhomogeneous plane waves in poro-viscoelastic media, explicitly incorporating both velocity and attenuation anisotropy. Starting from classical Biot theory, we present a fractional differential equation describing wave propagation in attenuative anisotropic porous media that accommodates arbitrary anisotropy in both velocity and attenuation. Then, instead of relying on the traditional complex wave vector approach, we derive new Christoffel and energy balance equations for general inhomogeneous waves by employing an alternative formulation based on the complex slowness vector. The phase velocities and complex slownesses of inhomogeneous fast and slow quasi-compressional (qP1 and qP2) and quasi-shear (qS1 and qS2) waves are determined by solving an eighth-degree algebraic equation. By invoking the derived energy balance equation along with the computed complex slowness, we present explicit and concise expressions for energy velocities. Additionally, we analyze dissipation factors defined by two alternative measures: the ratio of average dissipated energy density to either average strain energy density or average stored energy density. We clarify and discuss the implications of these definitional differences in the context of general poro-viscoelastic anisotropic media. Finally, our expressions are reduced to give their counterparts of the homogeneous waves as a special case, and the reduced forms are identical to those presented by the existing poro-viscoelastic theory. Several examples are provided to illustrate the propagation characteristics of inhomogeneous plane waves in unbounded attenuative vertical transversely isotropic porous media.

AbstractDeep learning (DL) approach has gained attention for earthquake (EQ) detection. To alleviate the problem of training data shortage, transfer learning (TL) provides a useful framework to adapt pre-trained models, typically through tuning of model parameters. Nonetheless, the current practice still requires considerable data, which hinders its application where only a small number of data is available. Instead of TL, we propose a novel two-stage of model correction as a solution to this important and ubiquitous problem in EQ detection. In the proposed approach, a pre-trained DL model is directly applied to waveform data in the target domain (first stage), and the cases that are classified as an earthquake signal (i.e., positive cases) are further classified as positives and negatives using a non-DL classification method (second stage). Our classification method for the second stage is based on multiple clustering, which characterizes local waveform patterns in terms of amplitude scale in specific time segments that are inferred in a data-driven manner. This characterization captures complex high-dimensional waveform patterns in a low-dimensional space, which leads to the effective classification of true and false positives. Furthermore, the proposed method is useful when only true positive waveforms are labeled (PU classification). Both synthetic and real data analysis clearly demonstrated effectiveness of unsupervised waveform characterization of the proposed method.

AbstractFull-waveform inversion (FWI) is a powerful imaging technique that produces high-resolution subsurface models. In seismology, FWI workflows are traditionally based on seismometer recordings. The development of fibre-optic sensing presents opportunities for harnessing information from new types of measurements. With dense spatial and temporal sampling, fibre-optic sensing captures the seismic wavefield at metre-scale resolution along the cable. Applying FWI to fibre-optic measurements requires the reformulation of the forward and adjoint problems due to two fundamental differences to seismometer data: i) fibre-optic measurements are sensitive to strain rather than translational motion, and ii) they do not represent the motion at a single spatial point, but instead capture the average deformation over a pre-defined cable segment, known as the gauge length. Within this study, we derive the adjoint sources to perform FWI for data from distributed acoustic sensing (DAS) and integrated fibre-optic sensing (IFOS) that are based on moment tensors. Our formulation incorporates gauge-length effects, direction-dependent sensitivity and complex cable layouts. For the numerical simulations, we use a spectral-element solver that allows us to incorporate surface topography, and coupled viscoacoustic and viscoelastic rheologies. In illustrative examples, we present how our theoretical developments can be used in inversions of synthetic fibre-optic data generated for a realistically curved cable placed on irregular topography. As examples, we invert for source parameters, including moment tensor, location, and origin time for noise-free DAS data, noise-contaminated DAS data, and IFOS data. Further, we present the 3-D imaging results for the three data groups and further analyse the effect of scatterers on the FWI based on DAS data. In all example inversions, we compare how close the found model is to the known ground truth. The codes to produce these results are accessible and ready to be applied to real data inversions.

AbstractHigh-frequency induced polarisation, which measures the complex electrical conductivity in a frequency range up to several hundred kHz, is potentially suitable to detect and quantify ice in the frozen subsurface. In order to estimate ice content from the electrical spectra, a two-component weighted power mean (WPM) model has been suggested and applied to field-scale data. In that model, ice is one of the components, whereas the solid phase, residual liquid water and potentially air form the second component, called “matrix”. Here, we apply the model to laboratory data previously discussed in the literature, with the aim to assess the applicability of the model and to understand the behaviour of the frequency-dependent electrical conductivity. The data were measured on an unconsolidated sediment sample with 20.8% water content from the European Alps, and a consolidated sandstone with 16.6% porosity. Electrical spectra have been measured over a temperature range from approx. - 41 ○C to +20 ○C and a frequency range from 0.01 Hz to 45 kHz. We extend the original WPM model to account for low-frequency polarisation in form of a constant phase angle model. The measured data were fitted with the model by a least-squares inversion algorithm. In order to reduce the ambiguity, we constrained several of the nine underlying parameters by literature values, in particular for the electrical properties of water ice, and the expected ice content according to porosity or water content of the unfrozen sample. Both data sets can be well matched, corroborating the hypothesis that the model is in principle suitable to explain measured data of frozen samples in that frequency range. One important observation is that the mixing parameter, i.e. the power in the WPM model, which is controlled by the geometric arrangement of the two components, depends on temperature. For the unconsolidated sample it even becomes negative at the coldest temperature, which is important because negative shape factors relate to specific geometries. A second observation is that relatively large permittivities of the matrix are required to fit the data, suggesting that processes at the interface between solid/liquid phase and ice, which are not included in the volumetric mixing model, might be relevant and should be considered in future extensions of the model.

Sea ice coverage in the Arctic Ocean is at one of its lowest levels on record, yet there's no unanimity on when that ice will disappear completely during summer months.

Sometime between 1381 and 1391, an earthquake exceeding magnitude 8.0 rocked the northeastern Caribbean and sent a tsunami barreling toward the island of Anegada.

Under the lead of the Leibniz Institute for Baltic Sea Research Warnemünde (IOW), a review article outlined the state of the Baltic Sea coast and its expected development as a result of climate change. The article shows that the Baltic Sea can serve as a model for the consequences of climate change and that interdisciplinary research is needed to investigate changes in its shallow coastal zones. The focus is on researching the interactions between the coastal area and the open ocean and the aim is to develop a basis for marine conservation measures. The feature article was recently published in the journal Estuarine, Coastal and Shelf Science.

Large changes in global sea level, fueled by fluctuations in ice sheet growth and decay, occurred throughout the last ice age, rather than just toward the end of that period, a study published in the journal Science has found.

A global research effort led by Colorado State University shows that extreme, prolonged drought conditions in grasslands and shrublands would greatly limit the long-term health of crucial ecosystems that cover nearly half the planet. The findings are particularly relevant as climate change increases the possibility of more severe droughts in the future, potentially leading to a situation that echoes the Dust Bowl of the 1930s.

The West Antarctic Ice Sheet (WAIS) is one of the most dynamic regions of the Antarctic continent. Much of its bed lies below sea level, making the region particularly sensitive to ocean warming. Understanding the development of the WAIS is central to anticipating future sea level changes. If the WAIS were to melt completely, global sea levels could rise by more than 4 meters.

The deal is done for the new underwater vehicle that will replace Ran, the submarine that was lost under a glacier in Antarctica in 2024. A large donation means that researchers at the University of Gothenburg can plan for new expeditions.

Tiny airborne particles known as aerosols, from biomass burning, urban pollution, and industrial emissions, can dramatically alter rainfall, cloud formation, and atmospheric stability. A new study led by Professor Kyong-Hwan Seo of Pusan National University, Korea, shows that aerosols profoundly reshape precipitation over the Maritime Continent, a region including Indonesia, Malaysia, Singapore, Vietnam, Thailand, the Philippines, and surrounding seas, where millions rely on predictable rainfall for water, food, and flood protection.

Increased warming in high-latitude wetlands seems poised to increase the activity of methanogens, or methane-producing microbes. These ecosystems are complex places, however, making outcomes hard to predict.

Dangerous flooding has damaged neighborhoods in almost every state in 2025, leaving homes a muddy mess. In several hard-hit areas, it wasn't the first time homeowners found themselves tearing out wet wallboard and piling waterlogged carpet by the curb.

A new study published in the journal Nature Communications reveals that the El Niño-Southern Oscillation (ENSO), a key driver of global climate variability, is projected to undergo a dramatic transformation due to greenhouse warming.

Source: AGU Advances

Increased warming in high-latitude wetlands seems poised to increase the activity of methanogens, or methane-producing microbes. These ecosystems are complex places, however, making outcomes hard to predict.

In new biogeochemical research taking into account tectonic, climatic, and ecological factors affecting the Copper River Delta in Alaska, Buser-Young et al. found that seismic uplift and glacial meltwater have each contributed to changes in microbial metabolism, with the surprising effect of potentially decreasing methane production.

The Copper River Delta in south central Alaska has a history of large seismic events. That includes, most recently, a 1964 earthquake that lifted portions of the delta to up to 3.4 meters above sea level, turning much of it from a marine environment to a freshwater one. In more recent decades, increasing amounts of iron-rich glacial runoff have also begun flowing through the delta, the result of climate change.

Combining geochemical studies of sediment cores from six wetland locations in the delta with metagenomic analyses of the microbes in the cores, the authors documented a distinct shift in microbial metabolism. Though genes for methanogenesis are still prevalent, and organic matter is available, they found that in an increasingly freshwater, iron-rich environment, the dominant means of energy production among the microbes shifted to involve iron cycling. Their findings are a demonstration of the ways large-scale geological and climatic shifts can affect small-scale processes such as the dynamics of microbial communities.

Looking ahead, the researchers say analyzing deeper sediment core samples could provide more information about how microbial dynamics have changed over time. In addition, they say, further culture-based experiments could improve understanding of the relationships between iron and organic matter within the carbon cycle. (AGU Advances, https://doi.org/10.1029/2025AV001821, 2025)

—Nathaniel Scharping (@nathanielscharp), Science Writer

Citation: Scharping, N. (2025), Tectonics and climate are shaping an Alaskan ecosystem, Eos, 106, https://doi.org/10.1029/2025EO250387. Published on 16 October 2025. Text © 2025. AGU. CC BY-NC-ND 3.0
Except where otherwise noted, images are subject to copyright. Any reuse without express permission from the copyright owner is prohibited.

“It’s a good thing we can’t hear it with our ears. Otherwise, we’d just have this constant din from the oceans.”

The steady thrumming of crashing waves is the ocean’s soundtrack. But behind that calming rhythm is a host of hidden chaotic sound waves, most of which are too low in frequency for humans to hear. This acoustic energy travels as infrasound through the air and as seismic waves through the ground. “It’s a good thing we can’t hear it with our ears,” said Stephen Arrowsmith, a geoscientist at Southern Methodist University in Texas. “Otherwise, we’d just have this constant din from the oceans.”

Recently, scientists developed a new method to monitor surf’s acoustic and seismic signatures to identify individual breaking waves within the noise. The findings could allow for new methods for monitoring sea conditions from land and even provide insights into conditions in the upper atmosphere.

A Signal in the Noise

Scientists first discovered surf-generated infrasound more than 20 years ago. One study, led by Arrowsmith, even detected infrasound more than 124 miles (200 kilometers) inland. While the number of such studies has slowed over the past decade, researchers at the University of California, Santa Barbara (UC Santa Barbara), who typically study volcano seismology realized they were well positioned to contribute to surf infrasound research. “We have the proximity to the coastline here on campus, so it seemed an interesting thing to explore,” said Robin Matoza, an Earth scientist and senior author on the paper.

While past studies had detected surf infrasound only as a continuous wall of noise, the researchers suspected that with new advances in computation as well as in acoustic and seismic detection, they could identify the acoustic signatures of individual waves.

The team, led by geologist Jeremy Francoeur, who conducted the work for his master’s thesis at UC Santa Barbara, installed a single infrasound sensor that collected near-continuous data for 10 months, from September 2022 to July 2023. Then, in October 2023, they conducted an intensive field experiment over 6 days, deploying a network of 12 infrasound sensors and one seismometer across a roughly 500-foot area near the Santa Barbara coast.

“One of the biggest surprises was that the same infrasound signals are being generated by surf nearly every day.”

The researchers also took GoPro videos to correlate specific ocean waves with the infrasound and seismic profiles they generated. They then selected the signatures of five waves as templates to match against the 10 months of single-sensor acoustic data, picking out individual crashing waves among all the infrasound recorded. “One of the biggest surprises was that the same infrasound signals are being generated by surf nearly every day,” said Francoeur in an email. The approach revealed up to tens of thousands of individual surf events per day.

“I liked how they were able to identify discrete surf events using this local array,” said Arrowsmith, who wasn’t involved in the new study. “Previous studies on this, including mine, were not able to do that.”

The researchers found they could detect discrete infrasound signals only when breaking waves were over approximately 6.5 feet (2 meters) high, suggesting that a minimum amount of energy is required to generate detectable infrasound. When waves were detectable, however, the size of the water’s waves correlated with acoustic signal strength. This finding was particularly noticeable in the winter months when larger storm swells reach the California coast.

By timing when infrasound signals hit each sensor in the network, the scientists triangulated the positions of the waves, pinpointing a hot spot of acoustic activity to a specific rocky reef area just offshore. This suggests that certain bathymetric features might be more effective than others at generating detectable infrasound. The findings were published in Geophysical Journal International.

From the Surf to the Sky

Monitoring and locating the infrasound signature of surf could offer a new method for monitoring sea conditions using land-based sensors, which is critical for maritime safety and coastal management and research. Sea conditions are most often studied using ocean-based buoys or video monitoring, which is obscured at night and in foggy conditions.

The new method could also have applications far beyond the coast. If the signals from individual waves can be detected at greater distances from shore, they could offer information about conditions in the upper atmosphere. This is possible because infrasound enters the upper atmosphere, and features like temperature and wind speed modulate the waves before they refract in the stratosphere and return to Earth.

By comparing the signatures of individual surf events detected at sensors positioned at different distances, scientists say it could be possible to correlate specific acoustic signals with atmospheric conditions, providing a new tool for studying weather patterns and atmospheric dynamics.

“If you have repetitive signals, you can monitor small changes in those signals,” Matoza said. “You could use that to infer changes in the atmosphere.”

—Andrew Chapman (@andrewchapman.bsky.social), Science Writer

Citation: Chapman, A. (2025), Scientists tune in to the ocean’s sound waves, Eos, 106, https://doi.org/10.1029/2025EO250384. Published on 16 October 2025. Text © 2025. The authors. CC BY-NC-ND 3.0
Except where otherwise noted, images are subject to copyright. Any reuse without express permission from the copyright owner is prohibited.