Broadband seismometers, though designed to record ground motion generated by earthquakes, are also sensitive to a wide range of other processes occurring at the interface between the solid Earth, oceans, and atmosphere, often considered noise. In the sub-seismic band (1–24 hours), they can detect tidal signals but are limited by self-noise for weaker Earth and atmospheric processes. By applying a coherence-based network stacking technique to large seismic arrays, we identify weak, periodic gravity signals at these frequencies. Using three years of collocated vertical seismic and pressure data from USArray, we demonstrate the atmospheric origin of these oscillations. Coherence and transfer function analysis reveal strong links between pressure and seismic acceleration at atmospheric tide periods. The transfer function shows frequency dependence consistent with superconducting gravimeter observations, and its consistently negative phase indicates that pressure increases correspond to decreases in gravitational acceleration. This confirms Newtonian attraction from atmospheric mass changes as the dominant mechanism. Our results show that network stacks of broadband seismometers can detect atmospheric gravity variations as small as 10–100 nanogals, demonstrating their value for gravimetry and for observing atmospheric dynamics. This approach also provides a framework to estimate atmospheric noise in the sub-seismic range, improving the detection of solid Earth signals once such contamination is removed.
<p>Future hydrological projections exhibit significant discrepancies among models, undermining confidence in the predicted magnitude and timing of hydrological extremes. Here we show that observation-constrained changes in global mean terrestrial water storage (TWS), excluding Greenland and Antarctica, could be approximately 83 mm lower than raw projections from the Inter-Sectoral Impact Model Intercomparison Project phase 3b (ISIMIP3b) by the end of this century under both the low (SSP1-2.6) and high (SSP3-7.0) future forcing scenarios. Notably, the 95th percentile upper bounds are substantially reduced from 2 to <span class="inline-formula">−</span>96 mm under the low-emissions scenario and from 8 to <span class="inline-formula">−</span>105 mm under the high-emissions scenario, revealing a notable overestimation of global freshwater availability in the raw model projections. Global models are intricate process representations, making it challenging to isolate causes of their differences with observations. However, by leveraging the emergent constraint (EC) methodology and inter-model spread to empirically adjust biases against observations, we derive more tightly constrained estimates of future TWS changes than those obtained from conventional, unconstrained approaches. The EC-corrected estimates are substantially lower than the raw ISIMIP3b projections, implying that current water resource planning may underestimate the severity of future water shortages, particularly if global water demand remains stable or continues to rise. Our findings pinpoint the urgent need to reduce model uncertainties and enhance the reliability of future hydrological projections to better inform water resource management and climate adaptation strategies.</p>
Mushfiqur Rahman, Sung Joon Maeng, Ismail Guvenc
et al.
Radio Dynamic Zones (RDZs) are geographically defined areas specifically allocated for testing new wireless technologies. It is essential to safeguard the regular spectrum users outside the zones from the interference caused by the deployed equipment within this zone. Previous works have utilized sparse reference signal received power (RSRP) measurements collected by unmanned aerial vehicles (UAVs) to construct a dense 3D radio map through ordinary Kriging. In this work, we illustrate that matrix completion can outperform ordinary Kriging. We partitioned a 2D area of interest into small square grids where each grid corresponds to a single entry of a matrix. The matrix completion algorithm learns the global structure of the radio environment map by leveraging the low-rank property of propagation maps. Additionally, we illustrate that the simple Kriging and trans-Gaussian Kriging yield better results when the density of known measurements is lower. Earlier works of RSRP prediction involved a training dataset at a single altitude. In this work, we also show that performance can be improved by utilizing a combined dataset from multiple altitudes.
Classical hydrodynamics rests on the point-particle idealization, leading to parabolic transport equations, infinite signal speeds, and the inability to capture finite time relaxation, anisotropic transport, or non Fourier thermal phenomena. This work introduces Extended Structural Dynamics (ESD), a kinetic framework in which constituents are described as spatially extended objects possessing orientation, angular momentum, and internal deformation modes. Starting from an extended Boltzmann equation, a Chapman Enskog expansion with BGK closure yields two hyperbolic parabolic transport laws: a dynamical spin equation coupling orientational relaxation to fluid vorticity, and a heat flux relaxation equation with structural thermal conductivity. These equations predict finite propagation speeds for momentum and heat, intrinsic shock regularization, anisotropic transport, and thermal waves. The spin equation provides a kinetic derivation of micropolar fluid theory, while the heat flux equation supplies a microscopic foundation for Cattaneo Vernotte behavior. Quantitative estimates indicate structural contributions can dominate classical transport coefficients. The BGK closure preserves the qualitative geometric structure of extended phase space and captures correct scaling; the connection between the orientational relaxation time and Lyapunov instability is established independently. The resulting scaling laws follow from rotational-translational coupling. Predictions include Mpemba crossover time for colloidal ellipsoids and shock width for asymmetric molecules, both testable with existing techniques.
This article concerns two 9‐week longitudinal case studies of the integration of an immersive blended‐learning structural geology curriculum at an Australian university. Immersive learning environments (ILEs) have the capacity to enhance learning outcomes in STEM through their inherent ability to represent 3D concepts and embed their users in real‐world scenarios. Virtual reality (VR) presents an opportunity for educators to complement students′ learning, particularly in disciplines such as geoscience that are dependent on spatial reasoning and experiential learning. However, there are significant challenges presented by the integration of ILEs into curriculum. This article evaluates the benefits and limitations of immersive blended learning through an in‐the‐wild exploration of instructor and student experiences. Overall, students were interested in learning through VR, enjoyed the experiences and improved their self‐efficacy. They preferred instructor‐led learning paradigms due to dynamic visualisations and engaged discussions. This article identifies several challenges that impacted integration and students′ learning and provides a list of recommended approaches for future curriculum integration.
The article proposes a metric for the analysis of video data recorded by an unmanned aerial vehicle that uses a structural similarity index for evaluation. The metric consists in comparing frames in terms of brightness, contrast and pixel structure and a subsequent assessment of the video frame state. A comparative analysis of the proposed and currently employed metrics was carried out. The research included simulations on analog and digital video data at different frame rates. The results showed that the developed metric successfully detects delays, frame distortions and dynamic changes in a video scene. The proposed metric can find a wide range of applications of unmanned aerial vehicles in applied areas: construction, agriculture, geology and cartography.
This experimental study examines the effects of landfill leachate contamination on soil hydraulic conductivity over a 12-month period, addressing the current lack of long-term experimental data in this field. Laboratory permeability tests were performed on sandy clayey silt samples contaminated with leachate at concentrations ranging from 5% to 25%. Microstructural and mineralogical analyses were conducted using SEM and XRD to identify the mechanisms behind observed changes. The results identify a critical threshold at 15% contamination, where soil behavior transitions from granular to cohesive characteristics. Hydraulic conductivity increases at low contamination levels (5–10%, up to 1.2 × 10<sup>−7</sup> m/s) but decreases significantly at higher levels (4.172 × 10<sup>−8</sup> m/s at 15%, 8.545 × 10<sup>−9</sup> m/s at 20%). These changes are controlled by contamination level rather than exposure time, with values remaining stable throughout the 12-month period. The study provides essential parameters for landfill design and contamination assessment, demonstrating how leachate concentration affects long-term soil hydraulic properties through mineral formation and structural modification.
Rapidly obtaining spatial distribution maps of secondary disasters triggered by strong earthquakes is crucial for understanding the disaster-causing processes in the earthquake hazard chain and formulating effective emergency response measures and post-disaster reconstruction plans. On April 3, 2024, a MW 7.4 earthquake struck offshore east of Hualien, Taiwan, China, which triggered numerous coseismic landslides in bedrock mountain regions and severe soil liquefaction in coastal areas, resulting in significant economic losses. This study utilized post-earthquake emergency data from China's high-resolution optical satellite imagery and applied visual interpretation method to establish a partial database of secondary disasters triggered by the 2024 Hualien earthquake. A total of 5 348 coseismic landslides were identified, which were primarily distributed along the eastern slopes of the Central Mountain Range watersheds. In high mountain valleys, these landslides mainly manifest as localized bedrock collapses or slope debris flows, causing extensive damage to highways and tourism facilities. Their distribution partially overlaps with the landslide concentration zones triggered by the 1999 Chi-Chi earthquake. Additionally, 6 040 soil liquefaction events were interpreted, predominantly in the Hualien Port area and the lowland valleys of the Hualien River and concentrated within the IX-intensity zone. Widespread surface subsidence and sand ejections characterized soil liquefaction. Verified against local field investigation data in Taiwan, rapid imaging through post-earthquake remote sensing data can effectively assess the distribution of coseismic landslides and soil liquefaction within high-intensity zones. This study provides efficient and reliable data for earthquake disaster response. Moreover, the results are critical for seismic disaster mitigation in high mountain valleys and coastal lowlands.
Geophysics. Cosmic physics, Dynamic and structural geology
Brittany N. Hupp, Mohammed S. Hashim, Raquel Bryant
et al.
Scientific ocean drilling (SciOD) has been invaluable in advancing our understanding of Earth history. However, the most recent international SciOD programme ended in 2024, alongside the non-renewal of the riserless drilling vessel, the JOIDES Resolution. The US has not committed to joining a new SciOD programme despite prior efforts focused on important scientific priorities (e.g. climate change, assessing natural hazards). During this critical juncture, we argue that incorporating accessibility, justice, equity, diversity and inclusion (AJEDI) efforts will further develop a cohesive community that is well prepared to tackle questions critical to the US and global community. Herein we provide recommendations to develop a knowledgeable and diverse community of scientists in the changing landscape of US SciOD, as informed by historical participation data and recent efforts by early career scientists. Recommendations focus on accessible training opportunities, enhanced stewardship of archived materials, additional funding for research at all academic levels, inclusion of cultural advisors and social scientists, and a commitment to continuing SciOD education. By pursuing these recommendations, the US SciOD community could become a leader for modelling AJEDI principles and ensuring equitable knowledge transfer that is needed to reimagine and rebuild a new, inclusive SciOD programme.
Simon Ghyselincks, Valeriia Okhmak, Stefano Zampini
et al.
Reconstructing the structural geology and mineral composition of the first few kilometers of the Earth's subsurface from sparse or indirect surface observations remains a long-standing challenge with critical applications in mineral exploration, geohazard assessment, and geotechnical engineering. This inherently ill-posed problem is often addressed by classical geophysical inversion methods, which typically yield a single maximum-likelihood model that fails to capture the full range of plausible geology. The adoption of modern deep learning methods has been limited by the lack of large 3D training datasets. We address this gap with \textit{StructuralGeo}, a geological simulation engine that mimics eons of tectonic, magmatic, and sedimentary processes to generate a virtually limitless supply of realistic synthetic 3D lithological models. Using this dataset, we train both unconditional and conditional generative flow-matching models with a 3D attention U-Net architecture. The resulting foundation model can reconstruct multiple plausible 3D scenarios from surface topography and sparse borehole data, depicting structures such as layers, faults, folds, and dikes. By sampling many reconstructions from the same observations, we introduce a probabilistic framework for estimating the size and extent of subsurface features. While the realism of the output is bounded by the fidelity of the training data to true geology, this combination of simulation and generative AI functions offers a flexible prior for probabilistic modeling, regional fine-tuning, and use as an AI-based regularizer in traditional geophysical inversion workflows.
The optimization-based damage detection and damage state digital twinning capabilities are examined here of a novel conditional-labeled generative adversarial network methodology. The framework outperforms current approaches for fault anomaly detection as no prior information is required for the health state of the system: a topic of high significance for real-world applications. Specifically, current artificial intelligence-based digital twinning approaches suffer from the uncertainty related to obtaining poor predictions when a low number of measurements is available, physics knowledge is missing, or when the damage state is unknown. To this end, an unsupervised framework is examined and validated rigorously on the benchmark structural health monitoring measurements of Z24 Bridge: a post-tensioned concrete highway bridge in Switzerland. In implementing the approach, firstly, different same damage-level measurements are used as inputs, while the model is forced to converge conditionally to two different damage states. Secondly, the process is repeated for a different group of measurements. Finally, the convergence scores are compared to identify which one belongs to a different damage state. The process for both healthy-to-healthy and damage-to-healthy input data creates, simultaneously, measurements for digital twinning purposes at different damage states, capable of pattern recognition and machine learning data generation. Further to this process, a support vector machine classifier and a principal component analysis procedure is developed to assess the generated and real measurements of each damage category, serving as a secondary new dynamics learning indicator in damage scenarios. Importantly, the approach is shown to capture accurately damage over healthy measurements, providing a powerful tool for vibration-based system-level monitoring and scalable infrastructure resilience.
The dynamic structural load identification capabilities of the gated recurrent unit, long short-term memory, and convolutional neural networks are examined herein. The examination is on realistic small dataset training conditions and on a comparative view to the physics-based residual Kalman filter (RKF). The dynamic load identification suffers from the uncertainty related to obtaining poor predictions when in civil engineering applications only a low number of tests are performed or are available, or when the structural model is unidentifiable. In considering the methods, first, a simulated structure is investigated under a shaker excitation at the top floor. Second, a building in California is investigated under seismic base excitation, which results in loading for all degrees of freedom. Finally, the International Association for Structural Control-American Society of Civil Engineers (IASC-ASCE) structural health monitoring benchmark problem is examined for impact and instant loading conditions. Importantly, the methods are shown to outperform each other on different loading scenarios, while the RKF is shown to outperform the networks in physically parametrized identifiable cases.
In view of the complexity and dynamics of structural safety risks during the construction and operation stages of swivel bridges, this study proposes an intelligent safety monitoring method based on the integration of Bayesian network (BN) and deep learning. By constructing a three-layer index system of "basic geology-rotation process-real-time monitoring", combining expert knowledge and engineering data to complete the learning of the network structure, and using the temporal convolutional network (TCN) to extract temporal features such as the rotation attitude and structural stress, the mapping of risk states is achieved. The (60+64) m swivel bridge of the North Extension project of Xi 'an Xingfu Road was taken as a case for verification. The results show that the fusion model is significantly superior to the single model in terms of indicators such as precision rate (0.91), recall rate (0.88), and AUC value (0.93), and the recognition ability for high-risk working conditions has improved by 32%. The research results provide quantitative tools for the precise control of structural safety during the construction and operation stages of swivel bridges, especially having significant advantages in dynamic risk prediction and multi-source monitoring data fusion under complex rotating processes.