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SummaryPassive surface wave method is increasingly being applied to urban subsurface exploration due to its non-invasiveness, low cost, and high efficiency. However, its imaging quality is often influenced by limited data acquisition time and the heterogeneous distribution of seismic ambient fields in complex urban environments. To extract coherent surface wave signals for seismic imaging in such challenging setting, we developed a multi-stage urban ambient noise deep clustering framework based on a convolutional autoencoder and deep embedded clustering algorithm. The initial clustering characterizes the distribution patterns of urban noise sources, which informs a secondary, finer clustering to select noise sources optimized for urban seismic imaging. Real-world experiment on the urban train noise field demonstrates our urban noise cluster framework effectively identifies and elucidates the temporal evolution patterns of moving train sources. Compared to traditional data selection methods, our approach yields superior dispersion measurements and significantly attenuates artifacts from the fundamental mode. Furthermore, by employing mode-specific clustering, we successfully capture the refined first overtone, enhancing the accuracy and depth resolution of seismic imaging. This study presents a new perspective to analyzing and selecting complex noise sources, significantly advancing seismic imaging and monitoring in alignment with emerging Artificial Intelligence trends.

SummaryBoth short-term coseismic off-fault damage and long-term fault growth during interseismic periods have been suggested to contribute to the formation and evolution of fault damage zones. Most previous numerical models focus on simulating either off-fault damage in a single earthquake or off-fault plasticity in seismic cycles ignoring changes of elastic moduli. Here we developed a new method to simulate the damage evolution of fault zones and dynamic earthquake cycles together in a 2D anti-plane model. We assume fault slip is governed by the laboratory-derived rate-and-state friction law while the constitutive response of adjacent off-fault material is controlled by a simplified version of the Lyakhovsky-Ben-Zion continuum brittle damage model. This study aims to present this newly developed modeling framework which opens a window to simulate the co-evolution of earthquakes and fault damage zones. We also demonstrate one example application of the modeling framework. The example simulation generates coseismic velocity drop as evidenced by seismological observations and a long-term shallow slip deficit. In addition, the coseismic slip near the surface is smaller due to off-fault inelastic deformation and results in a larger coseismic slip deficit. Here we refer to off-fault damage as both rigidity reduction and inelastic deformation of the off-fault medium. We find off-fault damage in our example simulation mainly occurs during earthquakes and concentrates at shallow depths as a flower structure, in which a distributed damage area surrounds a localized, highly damaged inner core. With the experimentally based logarithmic healing law, coseismic off-fault rigidity reduction cannot heal fully and permanently accumulates over multiple seismic cycles. The fault zone width and rigidity eventually saturate at long cumulative slip, reaching a mature state without further change.

How is ventilation at various depth layers of the Atlantic connected and what role do changes in ocean circulation play? Researchers from Bremen, Kiel and Edinburgh have pursued this question and their findings have now been published in Nature Communications.

A computer model drawing on satellite and climate data could give scientists an early warning of coastal marsh decline.

Using the model, scientists detected a decline in underground plant biomass across much of Georgia’s coastal marshes between 2014 and 2023. Critically, this loss occurred even though the marsh grasses appeared green and thriving at the surface.

The findings, published last month in Proceedings of the National Academy of Sciences of the United States of America, could help land managers identify targets for restoration before more severe damage takes hold.

Roots of Concern

Marshes “are not only economically but culturally and recreationally important places for the people who both live along the coast and visit the coast.”

Marshes “are not only economically but culturally and recreationally important places for the people who both live along the coast and visit the coast,” said study coauthor Kyle Runion, a landscape ecologist at the University of Georgia. They help control flooding, sequester carbon, and provide space for hunting, fishing, and wildlife spotting.

But rapid sea level rise has threatened coastal marsh grasses, as higher waters and more frequent flooding inundate the soil and choke oxygen supply at the roots. In a healthy ecosystem, underground plant biomass staves off erosion and adds organic matter that eventually decomposes into more soil, boosting the marsh’s resilience to sea level rise, so declining root systems can be an early sign of trouble in marshlands.

Marshlands can appear healthy even as their roots are dying off, said Bernard Wood, a wetland ecologist at the Coastal Protection and Restoration Authority of Louisiana who was not involved in the study.

A trip into the marsh itself tells a different story, however. “You could just pick up this huge clump of grass with one hand, and it barely has anything holding it to the ground,” Wood said.

Sea level rise can threaten the roots of smooth cordgrass, even as the leafy part of the plant can appear healthy. The exposed roots of smooth cordgrass are seen here at a marsh edge along the Folly River in Georgia. Credit: Kyle Runion/Colorado State University BERM and Biomass

To understand how Georgia’s marshes are responding to changing conditions, researchers developed and tested the Belowground Ecosystem Resilience Model (BERM) in 2021. BERM draws from satellite and climate data to estimate the belowground biomass of Spartina alterniflora, or smooth cordgrass, in coastal areas.

In the 2021 study, the team collected information on environmental conditions in Georgia salt marshes from Landsat 8, Daymet climate summaries, and other publicly available datasets. They built a machine learning model that could predict belowground biomass and trained it on field data from four marsh sites. Researchers found that elevation, vapor pressure, and flooding frequency and depth were some of the most important variables in predicting root biomass.

How a salt marsh looks on the surface isn’t necessarily an indicator of how it’s truly faring.

In the new study, Runion and his colleagues applied the model to estimate changes in S. alterniflora root biomass over nearly 700 square kilometers of Georgia coast between 2014 and 2023.

During that time, belowground biomass decreased about 1% per year on average, the team found. About 72% of the salt marsh area saw declines in underground plant mass. At the same time, aboveground biomass—the visible part of the marsh grass—increased over most of the study area.

The disparity between biomass above and below could occur because aboveground biomass is less sensitive to flooding than root systems. Or the increase might be temporary, as flooding initially delivers nutrients but eventually drowns the plant. In either case, how a salt marsh looks on the surface isn’t necessarily an indicator of how it’s truly faring.

Tool for Conservation

Early-warning signs of marsh decline provided by the model could be crucial for conservation. “Once [marsh] loss occurs, that can be irreversible,” Runion said. “By getting a sign of deterioration before loss happens, that’s when we can intervene and much more easily do something about this.”

Mapping which areas of the marsh are most vulnerable could also combat the tendency to see marshes as either “doomed” or “not doomed” and target conservation efforts to the areas most in need, said Denise Reed, a coastal geomorphologist at the University of New Orleans who was not involved in the study. Though belowground biomass is declining on average, some areas of the coast are experiencing less change than others.

“There are some complex patterns going on—probably something that it would be great to understand a little bit better,” Reed said. But “this idea of being able to detect areas which are in worse condition versus areas that are in better condition from the soil’s perspective is really helpful.”

For now, BERM can predict belowground biomass only in Georgia marshes. Other regions have different plant species and flooding dynamics that could alter the relationships BERM relies on. But with additional calibration data from other salt marshes, the team could make the model more widely applicable, Runion said.

“We are looking to expand this sort of modeling framework to include different species along the Gulf and East Coast,” Runion said.

—Skyler Ware (@skylerdware), Science Writer

Citation: Ware, S. (2025), Machine learning model flags early, invisible signs of marsh decline, Eos, 106, https://doi.org/10.1029/2025EO250253. Published on 17 July 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.

Author(s): T. A. Shelkovenko, I. N. Tilikin, A. R. Mingaleev, V. M. Romanova, and S. A. Pikuz

The small-sized, low-voltage, and low-inductive KING generator (190–230 kA, 40 kV, 200–240 ns) was specially designed to work with X-pinches; however, it was unstable in its original design. In the present work, it is experimentally shown that an increase in the inductance of the output node of the …

[Phys. Rev. E 112, 015207] Published Thu Jul 17, 2025

Clear-cutting can make catastrophic floods 18 times more frequent with effects lasting more than 40 years, according to a new UBC study.

When a hurricane is in the forecast, the National Oceanic and Atmospheric Administration (NOAA) deploys its famed Hurricane Hunter team to gather data directly from the storm. The team uses specialized aircraft to fly into the hurricane and collect information about its intensity, structure, and movement, which is used to improve forecasts and warnings.

Satellites circling Earth have many different functions, including navigation, communications and Earth observation. About 8%–10% of all active satellites are military or "dual use" serving intelligence or reconnaissance functions as spy satellites.

Samples of extremely small crystal clots, each polished to the thickness of a human hair or thinner, have revealed information about the process triggering the major 2006 eruption of Alaska's Augustine Volcano.

Monitoring changes in water temperature and pressure at the seafloor can improve understanding of ocean circulation, climate, and natural hazards such as tsunamis. In recent years, scientists have begun gathering submarine measurements via an existing infrastructure network that spans millions of kilometers around the planet: the undersea fiber-optic telecommunications cables that provide us with amenities like Internet and phone service.

The landslide that occurred in Blatten in the canton of Valais at the end of May 2025 and the one in the village of Brienz in Graubünden in June 2023 remind us of the potential for landslide hazards in the Alps. Debris flows are one such hazard. These flows of water, sediment and rock fragments typically occur after heavy rainfall in steep terrain, and rapidly travel down a channel, potentially destroying everything in their path.

Source: Geophysical Research Letters

Monitoring changes in water temperature and pressure at the seafloor can improve understanding of ocean circulation, climate, and natural hazards such as tsunamis. In recent years, scientists have begun gathering submarine measurements via an existing infrastructure network that spans millions of kilometers around the planet: the undersea fiber-optic telecommunications cables that provide us with amenities like Internet and phone service.

Without interfering with their original purpose, the cables can be used as sensors to measure small variations in the light signals that run through them so that scientists can learn more about the sea. Liu et al. recently developed a new instrument, consisting of a receiver and a microwave intensity modulator placed at a shore station, that facilitates the approach.

Transcontinental fiber-optic cables are divided into subsections by repeaters, instruments positioned every 50 to 100 kilometers that boost information-carrying light signals so that they remain strong on the journey to their destination. At each repeater, an instrument called a fiber Bragg grating reflects a small amount of light back to the previous repeater to monitor the integrity of the cable.

By observing and timing these reflections, the new instrument measures the changes in the time it takes for the light to travel between repeaters. These changes convey information about how the surrounding water changes the shape of the cable, and the researchers used that information to infer properties such as daily and weekly water temperature and tide patterns. Most previous work using telecommunications cables for sensing efforts treated the entire cable as a single sensor, and work that did use them for distributed sensing required ultrastable lasers. This instrument allowed the team to do distributed sensing using more cost-effective nonstabilized lasers.

The research team included geophysicists, electronics engineers, and cable engineers. They tested the instrument over 77 days in summer 2024 on EllaLink, an operation cable with 82 subsections running between Portugal and Brazil. As temperatures and tides rose and fell, the transatlantic cable stretched and contracted, providing measurable changes in the light traveling within it.

The study showed that the existing network of submarine cables could be a valuable resource for monitoring ocean properties, enabling everything from early tsunami warnings to long-term climate studies. (Geophysical Research Letters, https://doi.org/10.1029/2024GL114414, 2025)

—Saima May Sidik (@saimamay.bsky.social), Science Writer

Citation: Sidik, S. M. (2025), A transatlantic communications cable does double duty, Eos, 106, https://doi.org/10.1029/2025EO250252. Published on 16 July 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.

Glaciers hold layers of history preserved in ice, offering unique insights into Earth's past that can also help us interpret the future. Trapped amidst the frozen water are microscopic deposits of dust, pollen, and even pollutants that scientists can use to examine environmental changes through time.

New research led by the University of Victoria (UVic) has illuminated a significant and previously unrecognized source of seismic hazard for the Yukon Territory of northwestern Canada.

Nor'easters are powerful and often destructive cyclonic storms that primarily impact the East Coast of North America. Some of these weather events have been so fierce that they earned the names "Perfect Storm," "Storm of the Century," and "Snowmaggedon."

SummaryGreen’s function expressions for seismic interferometry in acoustic and elastic media have been extensively studied and applied across a wide range of applications, including surface-wave tomography and generating virtual shot gathers. However, analogous expressions for coupled acoustic-elastic media systems remain absent, despite their importance for analysing cross-correlation wavefields from ocean-bottom nodal and seismometer recordings and other seismic problems in marine settings. To address this issue, we derive convolution- and correlation-type reciprocity relations for physically coupled acoustic-elastic media by combining Rayleigh’s and Rayleigh-Betti reciprocity theorems, incorporating the constitutive equations governing coupling at the acoustic-elastic interface, and applying time-reversal invariance principles for an arbitrary 3-D inhomogeneous, lossless medium. The derived relationships show that the acoustic and elastic Green’s functions between any two observation points in the medium can be expressed as integrals of cross-correlations of wavefield observations at those locations, generated by sources distributed over an arbitrarily shaped closed surface enclosing the two observation points. When the Earth’s free surface coincides with the enclosing surface, integral evaluation is required only over the remaining portion of the closed surface. If the sources are mutually uncorrelated ambient sources, the Green’s function representation simplifies to a direct cross-correlation of wavefield observations at the two points, generated by a specific ambient source distribution on the closed surface. However, in practical scenarios, the ideal source distribution necessary to retrieve Green’s functions is rarely realized, for example, due to non-uniform illumination. To address these challenges, we represent the ambient cross-correlations as self-consistent observations and introduce a cross-correlation modelling methodology that accounts for practical limitations in source distribution for coupled acoustic-elastic media scenarios. We illustrate the theory by modelling ambient cross-correlation wavefields for a deep-water scenario.

SummarySeismic signals generated by near-surface explosions, with sources including industrial accidents and terrorism, are often analysed to assist post-detonation forensic characterisation efforts such as estimating explosive yield. Explosively generated seismic displacements are a function of, amongst other factors: the source-to-receiver distance, the explosive yield, the height-of-burst or depth-of-burial of the source and the geological material at the detonation site. Recent experiments in the United States, focusing on ground motion recordings at distances of <15 km from explosive trials, have resulted in empirical models for predicting P-wave displacements generated by explosions in and above hard rock (granite, limestone), dry alluvium, and water. To extend these models to include sources within and above saturated sediments we conducted eight explosions at Foulness, Essex, UK, where ∼150 m thicknesses of alluvium and clay overlie chalk. These shots, named the Foulness Seismoacoustic Coupling Trials (FSCT), had charge masses of 10 and 100 kg TNT equivalent and were emplaced between 2.3 m below and 1.4 m above the ground surface. Initial P-wave displacements, recorded between 150 and 7000 m from the explosions, exhibit amplitude variations as a function of distance that depart from a single power-law decay relationship. The layered geology at Foulness causes the propagation path that generates the initial P-wave to change as the distance from the source increases, with each path exhibiting different amplitude decay rates as a function of distance. At distances up to 300 m from the source the first arrival is associated with direct propagation through the upper sediments, while beyond 1000 m the initial P-waves are refracted returns from deeper structure. At intermediate distances constructive interference occurs between P-waves propagating through the upper sediments and those returning from velocity-depth gradients at depths between 100 and 300 m. This generates an increase in displacement amplitude, with a maximum at ∼800 m from the source. Numerical waveform modelling indicates that observations of the amplitude variations is in part the consequence of high P- to S-wave velocity ratios within the upper 150 m of saturated sediment, resulting in temporal separation of the P- and S-arrivals. We extend a recently developed empirical model formulation to allow for such distance-dependent amplitude variations. Changes in explosive height-of-burst within and above the saturated sediments at Foulness result in large P-wave amplitude variations. FSCT surface explosions exhibit P-wave displacement amplitudes that are a factor of 22 smaller than coupled explosions at depth, compared to factors of 2.3 and 7.6 reported for dry alluvium and granite respectively.

SummaryIntermittent fluid injection aims at inferring and steering hydraulic transmissivity and has become an integral part of reservoir stimulation techniques. Modeling the poroelastic response of such pumping operations poses new challenges with respect to the hydromechanical coupling. This is because when a fluid pressure perturbation is introduced in the pore space of a deformable porous rock, it will induce a stress perturbation in the solid phase and this is accompanied by pore boundary motion. Within the limits of quasi-static linear poroelasticity, we analyze the macroscopic signatures of pore boundary motion during injection, i.e., when the rock frame is mechanically loaded, and after injection stop, i.e., when pore boundaries tend to relax back into equilibrium. We show that there is a pumping sequence that allows to harness the energy associated with pore boundary motion accumulated during the frame-loading cycle. Our results foster the need to distinguish how pressure diffusion in poroelastic solids proceeds: either fluid transport is of compressible or incompressible nature and the respective diffusion constant depends on undrained or drained poroelastic moduli.

SummaryThermal properties such as thermal conductivity (TC), thermal diffusivity (TD), and specific heat capacity (SHC) are essential for understanding and modelling the subsurface thermal field. In sedimentary basins, these parameters play a key role in characterizing the present-day thermal state and predicting its evolution, for example, in response to future geo-energy utilizations. Given the wide range of potential geo-energy utilizations and the frequent lack of sufficient sample material, many studies have focused on developing accurate prediction approaches. Machine learning (ML) offers promising non-linear statistical methods to enhance the mapping of interrelations between standard geophysical well-log readings and thermal rock properties. In this study, we introduce an open-access tool for computing profiles of thermal rock properties from standard geophysical borehole logging data, building upon and extending previous petrophysical studies. The tool employs various machine-learning approaches trained on large, physically modelled synthetic datasets that account for mineralogical and porosity variability across major sedimentary rock groups (clastic rocks, carbonates, and evaporates). It establishes functional relationships between thermal properties and different combinations of standard well-log data, including sonic velocity, neutron porosity, bulk density, and the gamma-ray index. We trained four different models including linear regression, AdaBoost, Random Forest, and XGBoost using 80 per cent of the synthetic group data for model development, including training and hyperparameter tuning through cross-validation. The remaining 20 per cent was held out as an independent test set for statistical validation, feature recognition, and input variable importance analysis. A total of 15 input log combinations (including all one, two, three, and four well-log configurations) were evaluated across four machine learning models (linear regression, AdaBoost, Random Forest, and XGBoost), resulting in 180 trained models. The model's predictive accuracy and reliability were further evaluated against independent laboratory drill-core measurements reported in recent studies. Our results indicate that the best-performing predictive models vary depending on the available log-combinations. However, XGBoost frequently outperforms other models in sedimentary rocks. When at least two well logs are provided as input variables, the best-performing models predict thermal conductivity with an uncertainty below 10 per cent relative to borehole validation data (with laboratory-measured thermal conductivity). In most tested model cases and for most input log combinations, predictive errors for thermal conductivity range between 10 and 30 per cent at the (point measurement) sample scale (cm to half a meter). However, when averaged over geological formations or borehole intervals (tens to thousands of meters), the accuracy of thermal-conductivity predictions improves significantly, with uncertainties of the interval mean conductivity dropping below 5 per cent for large intervals. For specific heat capacity, prediction accuracy for the best-performing models at the measurement scale is typically better than 5 per cent. Thermal diffusivity reflects a larger variation, accumulating the uncertainties from conductivity and heat capacity. The presented log-based Python prediction tool provides an automated means to compute thermal parameters using the most suitable ML model for given well-log inputs, facilitating enhanced thermal characterization in sedimentary settings. This has practical relevance for geothermal or hydrocarbon exploration, or subsurface storage projects.

The Southwest United States is currently facing its worst megadrought of the past 1,200 years. According to a recent study by the University of Texas at Austin, the drought could continue at least until the end of the century, if not longer.