This summer broke records around the U.K. for maximum temperatures and dry weather. According to the U.K.'s Met Office, while it was rare to hit 35°C (95°F) in the 20th century, that temperature has been surpassed in six out of the past 10 years. And temperatures are only expected to get higher.
The weather over the North Atlantic affects hundreds of millions of people. From winter storms over Europe to cold spells in eastern North America, many of these impacts are shaped by large-scale atmospheric circulation patterns that recur. Climate scientists refer to these preferred patterns as atmospheric regimes.
In the humid tropics of Southeast Asia, afternoon thunderstorms roll through almost every day. Daytime heat causes towering, fluffy cumulonimbus clouds to form and swell before unleashing torrential rain on cities such as Singapore, Hong Kong and Bangkok. In these places, drainage systems are designed to cope with short, heavy downpours.
Isaac Larsen, an associate professor in the College of Natural Sciences' Department of Earth, Geographic, and Climate Sciences, is a lead author of "Status of the World's Soil Resources 2026," a new report produced by the Food and Agriculture Organization of the United Nations (FAO) that draws on recent scientific literature and contributions from hundreds of experts around the world.
SummaryIndustrial activities in active foreland fold-and-thrust belts complicate conventional seismic hazard assessment, yet how tectonic deformation and industrial perturbations affect fault stability remains poorly understood. The Hutubi underground gas storage facility, situated within the Hutubi anticline at the deformation front of northern Tian Shan, has experienced a marked increase in seismicity since the start of gas injection and extraction. Here, we integrate seismic-reflection interpretation, growth strata analysis, geomorphic surface survey and optically stimulated luminescence dating, and area-depth-strain analysis to reconstruct the structure and growth history of the Hutubi anticline and quantify its late Quaternary shortening rate. The Hutubi anticline is a detachment fold growing above an 8–9 km‐deep detachment. The shortening and uplift rates of anticline have undergone a marked acceleration, rising from long-term averages of ∼0.06 and ∼0.03 mm/yr initiated in the Pliocene to ∼1.1 and ∼1.3 mm/yr since ∼0.4 Ma, respectively. This acceleration, consistent with other foreland structures, reflects ongoing basinward propagation of the Tian Shan deformation front in the late Quaternary. Within this tectonic setting, we further modeled the Coulomb stress evolution on secondary faults within the anticline driven by tectonic deformation. Our results indicate that tectonic deformation drives secondary faults to the seismic triggering threshold over a decade, and governs where ruptures nucleate and how they propagate, consistent with relocated earthquake observations in previous studies. Given sustained late Quaternary activity and pronounced injection-extraction perturbations, the potential for moderate-to-strong earthquakes (∼Mw5.3–7.0) on these faults cannot be ignored in the future. Thus, in active tectonic regions, seismic hazard assessments must incorporate deformation rates to accurately evaluate tectonic deformation and industrial perturbation effects on fault stability.
Forest soils store an estimated 40% of the world's terrestrial carbon, but estimates suggest that around 15–30% of that soil carbon is lost when a forest is harvested. Tracking such losses can be tricky: wildfires, insects, disease and past forest management can all affect forest soil carbon stores, and tree type, climate and harvesting method can all affect how much carbon is lost.
When a prairie river carries more water in one year than another, the obvious place to look for an explanation is the sky. A year with more snow and rain should have more water in rivers. That was also how I initially thought about year-to-year changes in river flow. But while studying the Prairie Pothole Region, I learned that this is only part of the story. What happens after water reaches the ground can be just as important as how much falls from the sky.
Water does not flow only through rivers on the ground. Vast quantities of water also move through the atmosphere, carried by winds from oceans and land before eventually falling as precipitation hundreds or even thousands of kilometers away. These persistent pathways of atmospheric moisture are often referred to as "aerial rivers" or "flying rivers."
Kauri are among Aotearoa New Zealand's tallest and longest-lived trees—and their place in our cultural landscape looms equally large.
Author(s): Lehui Cao, Haibin Tang, Junxue Ren, Zhiyuan Chen, Guangchuan Zhang, Yibai Wang, Yuzhe Sun, and Jinbin Cao
The induced magnetic field in an applied field magnetoplasmadynamic discharge device is a key physical quantity for plasma transport and electromagnetic feedback in the plasma discharge process. To clarify its spatial evolution and the associated driving mechanisms under steady operating conditions,…
[Phys. Rev. E 114, 045203] Published Mon Oct 05, 2026
SummaryWhile well-log data provide direct measurements of subsurface properties essential for building lithofacies models, their sparse distribution and limited availability in exploration areas introduce significant uncertainty. This study presents an approach that integrates seismic data, rock physics, machine learning, and geostatistics to construct a regional-scale three-dimensional (3D) lithofacies model in the Jeju Basin, located offshore the southern Korean Peninsula. The proposed workflow comprises three integrated steps: performing Simultaneous Pre-Stack inversion on processed seismic data to derive rock properties, training neural networks for lithofacies prediction, and expanding the two-dimensional (2D) predictions into a 3D lithofacies model using Sequential Indicator Simulation. The prediction networks reproduced the interpretation-derived lithofacies labels with reasonable accuracy for the primary sedimentary classes, as demonstrated by quantitative evaluation metrics. The resulting 3D lithofacies model captured key geological features indicated by 2D seismic data, including faults and sedimentary structures, exhibiting improved spatial continuity and detail compared to conventional well-log-only geostatistical simulation. Comparison with a borehole outside the training volume demonstrated reasonable agreement between the predicted and well-log-derived lithofacies. This observation preliminarily implies the workflow’s applicability to field data beyond the 3D seismic coverage, though this assessment is based on a single well location. This integrated approach offers a cost-effective solution for lithofacies characterisation in frontier exploration sites where regional 3D seismic surveys are economically unfeasible and well control is limited.
SummarySeismic inversion plays a critical role in oil and gas exploration and CO2 storage monitoring by providing accurate subsurface structural imaging and quantitative characterization of rock properties, which offer valuable insights into reservoir characteristics, fluid flow, and storage integrity. However, traditional model-driven approaches are computationally expensive and heavily reliant on accurate initial models, which limits their applicability in complex geological settings. Furthermore, the need for manual hyperparameter tuning and expert intervention impedes automation and reduces their suitability for time-sensitive tasks such as real-time monitoring. Recent advancements in deep learning have demonstrated significant potential for seismic inversion by enabling neural networks to capture complex, nonlinear relationships between seismic data and subsurface properties. However, current data-driven methods still face major challenges, including limited generalization across diverse geological settings, poor interpretability due to the black-box nature of neural networks, and inadequate control over resolution and inversion fidelity. To address these limitations, we propose an implicit seismic inversion (ISI) approach that models subsurface physical parameters as a continuous function, implicitly represented by a deep neural network. Specifically, this study focuses on both post-stack impedance inversion, which recovers acoustic impedance as the key physical property, and pre-stack AVO inversion, which simultaneously inverts for P-wave velocity, S-wave velocity, and density. In this way, ISI leverages the learning dynamics of neural network optimization to reduce dependence on prior information, while enhancing interpretability and generalization ability through physics-informed constraints. Moreover, the continuous nature of the representation can significantly reduce the computational burden without compromising inversion accuracy. Experimental results on both synthetic and field datasets demonstrate that the proposed ISI method surpasses existing approaches in terms of accuracy, convergence, and independence from prior information. These advantages position ISI as a promising framework for scalable, automated, and real-time seismic inversion in next-generation geophysical workflows.
SummaryGeomagnetic data assimilation provides a framework for estimating the Earth’s core dynamics by combining geomagnetic observations with numerical geodynamo models. While the ensemble Kalman filter (EnKF) has been widely used as a sequential assimilation method, whether the ensemble Kalman smoother (EnKS), which additionally exploits future observations, improves estimates of the core state in fully nonlinear geodynamo models remains unclear. Here we compare the EnKS with the EnKF using synthetic observation experiments in three-dimensional geodynamo simulations under identical localization and inflation strategies. Under perfect-model conditions, the EnKS converges more rapidly and recovers additional flow modes, yielding more accurate estimates of both the internal velocity field and the core-surface flow. In contrast, the EnKS exhibits filter divergence in the magnetic field during the late stages of assimilation, reflecting its greater sensitivity to sampling errors. We then examine the influence of model error by introducing viscosity differences between the truth and assimilation models while maintaining otherwise nearly identical dynamics. Viscosity-induced model error degrades the EnKS, leading to reduced accuracy in recovering the dominant velocity modes and renewed filter divergence, although the large-scale core-surface flow remains well recovered. These results demonstrate that the EnKS can outperform the EnKF when the ensemble adequately represents the forecast-error covariance, but its more extensive use of observational information also makes it more susceptible to sampling and model errors. Improving the description of error covariances is therefore essential for realizing the full potential of EnKS-based geomagnetic data assimilation.
SummaryPassing trains act as correlated sources in both space and time, generating powerful waves that illuminate the near-surface below critical infrastructure. We propose an automated workflow for surface wave imaging below railway tracks that uses passing trains as the seismic source, in a context where train signals are far from an ideal isotropic source distribution. We exploit a dense 2D seismic array situated a few meters from the tracks for 3D imaging. To accommodate the complex moving source, we retain only correlations that form a limited angle with the moving train. This extends existing methods for a single line of receivers, either parallel or perpendicular to the tracks. We split the data into two cases, where the train is either far from or close to the acquisition setup. When the train is far from the array, sensor pairs mainly parallel to the tracks are important, while sensor pairs perpendicular to the tracks are decisive otherwise. In each case, the signals are time-windowed before computing the cross-correlations. Local dispersion panels are obtained through a Matched Field Processing algorithm. The dispersion curves are computed for various subsets of the array and referred to as patches. Extracting the dispersion curve of the surface wave below many patches results in a three-dimensional model of the Rayleigh wave phase velocity of the near-surface. Analyzing correlations for trains both near and far from the array yields broadband Rayleigh wave dispersion curves with high-frequency content (above 70 Hz), which is uncommon in passive methods. The velocity model is robust for wavelengths as short as 3 m and sensitive to soils at around 1 meter depth, allowing to image what may be a thicker ballast layer formed by previous track settlement issues.
SummaryConvection in Earth’s liquid outer core is powered by a combination of thermal and compositional buoyancy released at the inner-core boundary (ICB). The interaction between these two buoyancy sources, known as thermochemical convection, governs the strength of core flow and ultimately controls magnetic field generation. We present three-dimensional simulations of rotating convection in spherical shells that explicitly incorporate this coupling through a flux condition linking temperature (T) and composition (χ) at the ICB, expressed as $\partial \chi / \partial r = \mathcal {C}\, \partial T / \partial r$, where $\mathcal {C}$ is the coupling coefficient and r is the radial coordinate. By systematically varying $\mathcal {C}$ and the aspect ratio ζ = ri/ro (with ri and ro denoting the inner and outer radii respectively), two distinct flow regimes are identified: a viscous-dominated regime at weak coupling, primarily driven by thermal buoyancy, and an inertia-dominated regime at strong coupling, driven by compositional variations. Extrapolation of these dynamical regimes from the parameter values explored here to Earth’s actual Ekman number requires caution. We find that an effective coupling parameter $\mathcal {C}_{\mathrm{eff}} = \mathcal {C}\, \Lambda \, [3\zeta ^2(1-\zeta )/(1-\zeta ^3)]$, which combines the effects of buoyancy flux coupling and geometry through the buoyancy ratio Λ (ratio of compositional to thermal forcing across the domain), collapses the kinetic-energy $(\mathcal {K})$ of all models, which span a range of radius ratios, into universal scaling relationships: $\mathcal {K} \propto \mathcal {C}_{\mathrm{eff}}$ in the viscous regime and $\mathcal {K} \propto \mathcal {C}_{\mathrm{eff}}^{2/3}$ in the inertial regime. Diagnostics of the local Rossby number and of the momentum budget corroborate a transition in the dominant force balance when $\mathcal {C}_{\mathrm{eff}}$ exceeds unity. These results suggest that the geodynamo transitioned from thermally to compositionally dominated convection soon after the onset of inner-core growth (0.5–1.1 Gyr ago): for the midline material parameters the $\mathcal {C}_{\rm eff}=1$ crossing occurs within ~10–20 Myr of nucleation (inner-core radius ~85 km), with the full uncertainty band depending primarily on the poorly constrained effective compositional diffusivity. This transition may be apparent in the paleomagnetic record. The derived scaling laws establish a quantitative link between inner-core evolution, ICB thermochemical coupling, and the vigor of core convection, providing physically grounded benchmarks for models of planetary dynamos and core evolution.
SummaryThe central United States has experienced a surge in induced seismicity linked to wastewater injection, with Oklahoma as a key study area. While the broader effects of large-scale injection are well documented, the relationship between local injection activities and short-term seismicity fluctuations is less understood. Using deep learning and dense seismic monitoring, we developed an enhanced earthquake catalog and identified four persistent, low-magnitude (ML= – 0.69 to 2.83) seismic clusters within an active saltwater-disposal region. These clusters, several located near mapped faults, exhibit near-daily activity and provide case studies for examining local injection–seismicity dynamics. We find that injection histories for wells within approximately 8–10 km of the clusters generally exhibit stronger temporal correspondence with seismicity than those aggregated over larger spatial scales. Analysis of individual wells further identifies injection histories exhibiting comparatively strong short-term temporal correspondence with observed seismicity, independent of average injection rate or well distance. These relationships are interpreted as statistical associations within a pre-existing regional stress and pore-pressure state rather than evidence of direct hydraulic causality. Compared with higher-moment induced-seismicity cases, the detected clusters exhibit comparable patterns of cumulative seismic moment growth but at predominantly lower magnitudes. These low-moment sequences demonstrate how deep learning–augmented, high-resolution earthquake catalogs can reveal short-term variations in seismic response that are poorly resolved by conventional regional catalogs. Our results suggest that temporal similarity metrics may complement established injection-rate and distance criteria by identifying individual wells for further evaluation in induced-seismicity monitoring and mitigation strategies.
SummaryIn August 2024, Global Navigation Satellite System (GNSS) stations observed changes in the displacement pattern of the Santorini Volcanic Complex, accompanied by microseismic activity, showing a potential sign of unrest. Here, we analyze and discuss surface deformation patterns related to the Santorini caldera unrest, spanning from summer 2024 to January 2025. Synthetic Aperture Radar (SAR) interferometry was performed exploiting the Persistent Scatterer Interferometry (PSI) technique. We used both satellite geometries of the Sentinel-1 mission, producing line-of-sight (LOS) deformation maps and displacement decomposition in vertical and horizontal components. The observed displacements were combined with measurements from seven GNSS stations across the Santorini Volcanic Complex. The results highlight horizontal movements and uplift up to 70 mm/yr with a radial inflation pattern centered around the Kameni Islands, reminiscent of the 2011-2012 inflation episode. The geophysical model for the 2024-2025 period suggests a positive volume rate change at 3.15 km depth. Compared to the 2011-2012 unrest, the volume rate change is smaller; potentially a mix of magma and gas may be causing pressure along Kameni’s volcano-tectonic line, this time with a shallower source than the previous unrest. We address this particular period as the ”first phase” of the 2024-2025 unrest, since subsequent GNSS observations reveal a shift in the deformation, indicating the initialization of a different displacement pattern.
SummaryAccurate source localization is critical for monitoring volcanic tremors, microseismic events, and earthquakes. Compared to other source localization algorithms, waveform-based interferometric imaging exhibits superior robustness. It proves particularly effective when confronting weak signal phase signatures, noise interference, and inaccurate velocity models. Despite shared methodological commonalities, high-resolution imaging and localization of volcanic tremors prove notably more challenging than other passive seismic tasks. This difficulty primarily arises from sparse monitoring networks, complex wavefield interactions with the subsurface, and the indistinct phase signatures inherent in tremor signals. Consequently, waveform-based interferometric imaging tends to suffer from more severe blurring effects (i.e., poorer spatial resolution) when applied to volcanic tremor localization and compared against other application fields. To address these resolution limitations, we propose a maximum likelihood estimation (MLE)-based iterative peeling interferometric imaging method. This approach synergizes the inherent noise robustness of interferometry with a rigorous iterative framework designed to deblur the source image. By constructing data-driven empirical wavelets and employing an adaptive whitening filter dynamically updated via the Sherman-Morrison formula, the method sequentially strips source responses from the residual wavefield. This adaptive procedure directly counters the energy smearing caused by sparse arrays, effectively suppressing sidelobe interference to achieve a highly focused source resolution. Synthetic tests based on the Katla scenario demonstrate that the proposed method yields significantly higher spatial resolution than existing techniques. Furthermore, it exhibits robust performance even under adverse array geometries, concurrent multi-source interference, and velocity model inaccuracies. Field application to the 2011 Katla data further validates this by recovering source locations consistent with known ice cauldrons.
SummaryEarthquake early warning requires reliable uncertainty estimates from short initial waveforms. We evaluate Split conformal prediction (CP) and conformalized quantile regression (CQR) for single-station peak ground acceleration using event-partitioned K-NET records. Each 3 s waveform is normalized by component while retaining amplitude scales measured before normalization. The convolutional neural network (CNN)–Transformer that retains amplitude information is more accurate and yields narrower intervals than the waveform-only ablation. At the nominal 90 per cent level, CQR attains 90.03 per cent coverage; raw quantile regression and Split CP are also near nominal, whereas heteroscedastic Deep Ensemble and MC-Dropout baselines are more conservative and wider. Event-cluster bootstrap and repeated event-level resplits support marginal-coverage stability, although performance degrades for rare large earthquakes. Upper-bound screening trades increased recall for more false alarms, so the intervals are an uncertainty-quantification and screening layer rather than a standalone alert system. Under exact exchangeability, conformal prediction supplies finite-sample marginal coverage; record-level exchangeability is not established for this event-clustered sample, and event-conditional reliability is not guaranteed.
SummaryInductive electromagnetic (EM) methods induce currents in the ground with a changing magnetic field associated with time-varying current flowing in a transmitter loop. Geophysicists use measurements of the secondary magnetic field to estimate the conductivity structure of the ground. These estimations often assume that conductivity is constant, but in some cases, the conductivity can be dispersive and vary as a function of frequency. In this case, the dispersion is called Induced Polarisation (IP), and with an inductive (EM) source is called Inductive Induced Polarization abbreviated as IIP. IP and IIP phenomena are usually caused by electrochemical diffusion associated with small capacitor-like components in the ground. These components may be metallic particles, graphite, clay or local restrictions in current flow paths. Capacitor-like elements result in induced electrical polarisation or charge separation associated with current flow. When the injected or induced currents have turned off or decayed, the separated charges return to their original equilibrium state. This results in a net flow of current, called polarisation current, in the opposite direction to the original secondary current flow. The most common way of characterising IP and IIP is to estimate the parameters of a mathematical model that fits the measured data. The Cole-Cole model is the most common. Reversed or return polarisation current flow manifests itself in time-domain (transient) EM data as negative amplitudes or as positive decays that decay faster at late-time decays than is possible from a non-polarizable source. Depending on whether excitation is from a current or a voltage source, observed decays may have time constants that differ by orders of magnitude. For inductive sources, characterisation is best achieved by fitting a model that is a perturbation from the high-frequency conductivity rather than a perturbation from a low-frequency resistivity, with the associated Cole-Cole conductivity time-constant most directly associated with grain size. Sign reversals or negative responses from return current flow have been recorded since the 1970s or earlier in ground central-loop EM data, and in airborne EM data since the 1990s. Prior to the development of inversion methods, IIP negative transients were often simply removed from field data, leading to inaccurate conductivity models and interpretation. Since the turn of the century, modelling and inversion of Cole-Cole parameters have been common and applied to measured ground, semi-airborne and airborne transient EM data to parameterise any IIP effects. In order to explain the distortion of decays by IIP phenomena, IP-incorporated modelling and inversion of both the source and its host materials are critical for valid characterisation and interpretation. However, overparameterization and the limited bandwidth can lead to model uncertainty and ambiguous results. This is particularly the case if large-scale 3D inversion is attempted. We review IIP field examples for minerals of economic interest that may have IP associations, including disseminated sulphides, gold-associated alteration and kimberlite pipes under permafrost conditions. Of importance in near-surface, hydrogeological and contaminant mapping are other polarizable sources, and IIP has been applied to mapping clays, hydrocarbons and permafrost.
