Search Results (860)

GNSS-derived strain-rate analysis, geodetic earthquake recurrence modeling, and seismic potential estimations were integrated to investigate segment-scale deformation behavior along the central North Anatolian Fault Zone (NAFZ) using a high-resolution geodetic velocity field. The obtained strain rates reveal that deformation within the central NAFZ is distributed across a geometrically complex and kinematically heterogeneous fault network rather than being restricted to the main fault strand alone. While the main fault accommodates the majority of regional deformation, significant strain accumulation is also observed along major splay fault systems, including the Merzifon–Esençay, Ezinepazarı, Sungurlu, Eldivan, and Ekinveren faults. The derived strain patterns further indicate the coexistence of localized transtensional and transpressional deformation regimes controlled by fault geometry, segment boundaries, and structural discontinuities. Geodetically derived earthquake recurrence periods display pronounced spatial variability, with shorter recurrence periods concentrated along the main fault strand and comparatively longer earthquake cycles characterizing structurally complex splay systems. Among the investigated structures, the eastern and central segments of the Merzifon–Esençay Fault (MEF) exhibit relatively elevated strain accumulation and seismic potential. In particular, the estimated potential earthquake magnitudes reaching Mw 7.3–7.5, together with paleoseismological evidence indicating that the most recent major surface-rupturing event along the Esençay segment occurred approximately 3700 years ago, suggest that this fault system may represent a candidate seismic gap within the central NAFZ. Overall, the results demonstrate that deformation within the central NAFZ is strongly partitioned among interacting fault segments and highlight the importance of segment-scale geodetic analyses for improving seismic hazard assessments in complex strike-slip fault systems. Full article

A massive number of early-constructed small-to-medium-span bridges are collectively entering an “aging” phase in China. Meanwhile, vast amounts of unstructured bottom-level inspection texts remain underutilized. To address them, this paper proposes a data governance method. Large Language Models were leveraged to process unstructured defect data from 18,238 real-world bridges nationwide. The data were structurally cleaned and mapped into discrete features, revealing multidimensional vulnerabilities. On this basis, the Stable Contrastive Pattern Risk (SCPR) intelligent decision-making model was developed. The results demonstrate that, following robust filtration, 6 nationwide common risk rules were extracted from 2064 initial candidate combinations. These rules converge into three core risk patterns: the heavy-duty aging pattern, the substructure-dominated pattern, and the over-water small-span low-seismic-design pattern. Guided by these robust rules and specific damage enrichment characteristics, risk stratification and differentiated management strategies were further formulated for Class III bridges. This research facilitates a paradigm shift in bridge maintenance. It moves from reactive, post-event symptom characterization toward data-driven, proactive early warnings. This shift provides a substantive scientific foundation for optimizing resource allocation and enabling precise investment decisions at the road network level. Full article

Seismic attribute selection remains a critical yet often heuristic component in deep learning-based segmentation workflows. In this work, we propose a redundancy-aware framework to systematically analyse the contribution of seismic attributes by combining input-space statistics, representational similarity (CKA), and error-based evaluation. Our results suggest that statistical redundancy in the input space does not directly translate to functional redundancy within the network. In particular, attributes such as amplitude and instantaneous phase may exhibit high similarity in the input space while producing distinct error patterns and meaningful performance gains. We further observe that complementary attributes do not necessarily yield additive improvements. While some combinations introduce conflicting interactions that limit global performance, others provide stable and consistent improvements across classes. Notably, the combination of amplitude, phase, and local variance forms a minimal informative subset that improves segmentation performance in a balanced manner, particularly in challenging facies. These findings suggest that attribute selection should be guided by functional complementarity and interaction stability rather than by input diversity alone. The proposed framework provides a principled approach for identifying effective attribute subsets, contributing to more efficient and interpretable seismic segmentation workflows. Full article

Laser remote sensing based on wavefront sensors shows great potential for detecting minute vibrations. However, due to their high detection sensitivity, wavefront sensors are highly susceptible to interference from environmental noise and instrument-induced noise, which significantly compromises the quality of the acquired vibration signals and the accuracy of the detection. In this study, over 60,000 vibration signal data samples were collected under various amplitude and frequency conditions using a laser remote sensing seismic wave detection system. By applying a physics-aware dual-channel decoupled network (PRISM) to perform noise reduction on the vibration signals, we achieved improvements in signal quality under multiple real-world noise environments. The average signal-to-noise ratio improved by 12.16 dB, and the signal distortion ratio improved by 6.35 dB, successfully preserving faint vibration signals within the noise. Full article

Orthorhombic normal fault networks in sedimentary basins and their formation mechanisms are of significant geological importance. Orthorhombic normal fault networks and planar H-shaped normal fault networks (HNF) developed in distinct locations along the Linshang dextral transtensional fault, with the HNF on the eastern hanging wall and the orthorhombic normal fault networks on the western footwall. Using 2D and 3D seismic data, we investigated the geometry, evolution, and formation mechanisms of these fault networks within the regional tectonic context. The HNF consists of systematically arranged E–W-trending normal faults and N–S-trending cross normal faults. The orthorhombic normal fault networks comprise four sets of normal faults. Expansion indices and balanced cross-section analyses indicate that both these networks formed contemporaneously during the E₂s³ stage. Mechanical analysis suggests that differences in the local stress field led to the development of these networks in different segments of the Linshang Fault. The HNF formed sequentially within a single tectonic phase. In contrast, the orthorhombic normal fault networks developed within a 3D strain field driven by the combined effects of dextral transtension along the Linshang Fault and footwall tilting. Hydrocarbon exploration results confirm that these normal fault networks exert significant control on hydrocarbon migration pathways and accumulation patterns. Full article

The generation of synthetic seismic accelerograms is a critical problem in earthquake engineering, where the scarcity of strong-motion records, particularly for high-magnitude and near-fault scenarios, limits the reliability of structural analyses and probabilistic seismic hazard assessments. This paper presents a proof-of-concept wavelet-decomposed conditional Generative Adversarial Network (WD-cGAN) for the synthesis of seismic accelerograms that reproduce the physical and statistical properties of real ground-motion records. Unlike prior GAN-based approaches that rely on Fourier-domain decomposition, the proposed architecture decomposes each training signal into N wavelet sub-bands (experimentally $N=7$, six detail sub-bands D1–D6 and one approximation sub-band A6) using the Daubechies-4 (db4) discrete wavelet transform (DWT), assigning each sub-band to a dedicated discriminator. A novel energy-based weighting scheme ${\alpha }_i$ modulates the relative contribution of each discriminator to the total generator loss, ensuring that physically dominant, low-frequency bands, which carry the bulk of seismic energy, receive proportionally higher training emphasis. Seismic moment magnitude $M_w$ serves as the primary conditioning variable, enabling targeted synthesis for specific hazard scenarios. The model is implemented in Python v3.9 using PyTorch v.2.10 and trained on accelerograms drawn from the Italian INGV/ITACA v4.0 archive. Preliminary evaluation on 500 synthetic accelerograms across five magnitude classes provides evidence that the proposed wavelet-domain multi-discriminator scheme reproduces the essential spectral shape and non-stationary temporal structure of real ground-motion records within the considered magnitude range; full quantitative validation on a larger and more diverse corpus, rigorous comparison with competing methods, and extended multi-parameter conditioning are identified as the principal avenues for future work. Full article

The seismic fragility of reinforced concrete (RC) bridge piers is critical for urban rail transport systems, as severe pier damage may interrupt post-earthquake operation and threaten network safety. Compared with conventional highway bridge piers, urban rail transport RC solid piers usually have lower axial load ratios, larger cross-sections, and stricter serviceability requirements. However, the combined effects of geometric parameters, reinforcement detailing, and material strength on their cyclic behavior, dynamic response, and seismic fragility remain insufficiently understood. To address this issue, seven 1/4-scale RC solid pier specimens were tested under quasi-static cyclic loading to examine the effects of pier height, transverse reinforcement ratio, and longitudinal reinforcement ratio on damage evolution, hysteretic response, skeleton curves, and energy dissipation. A fiber-based OpenSees model considering bond-slip effects was then established, validated against the tests, and extended to a full-scale prototype pier for parametric analysis. The effects of aspect ratio, axial load ratio, longitudinal reinforcement ratio, stirrup ratio, steel yield strength, and concrete strength were evaluated under cyclic loading and nonlinear dynamic time-history excitations. An incremental dynamic analysis-based probabilistic seismic demand model was further developed using 30 near-fault ground motions, with peak ground acceleration as the intensity measure and displacement ductility as the engineering demand parameter. The results showed that increasing the aspect ratio changed the failure mode from flexure-shear-dominated to flexure-dominated behavior, increasing the ultimate displacement from 122 mm to 155 mm while reducing the peak lateral strength from 263 kN to 248 kN. Increasing the longitudinal reinforcement ratio improved both peak strength and ultimate displacement, from 226 kN to 262 kN and from 120 mm to 160 mm, respectively. The numerical results indicated that aspect ratio, axial load ratio, and longitudinal reinforcement ratio had more pronounced effects on seismic demand and fragility than stirrup ratio. Increasing steel yield strength generally reduced seismic fragility, whereas increasing concrete strength enhanced lateral resistance but did not necessarily improve fragility performance. These findings suggest that the seismic performance of urban rail transport RC solid piers should be evaluated by combining cyclic response, dynamic demand, and fragility-based performance, rather than by maximizing any single design parameter. Full article

Near-fault ground motions (NFGMs), characterized by forward-directivity velocity pulses, impose severe kinematic demands that challenge conventional structural systems. As modern civil engineering pivots toward rapid functional recovery, a critical paradigm shift is required: moving from component-centric kinematic vulnerability diagnostics to network-level systemic resilience optimization. This comprehensive review elucidates this transition, conceptualizing an integrated “3C Resilience Framework”—encompassing Coupled-multi-hazard, City-scale, and Carbon-friendly dimensions—as a strategic roadmap for next-generation seismic design. A pivotal focus is the physical evaluation of contemporary regulatory evolutions, specifically the multi-point spectral lower-bound constraints in American Society of Civil Engineers Standard 7-22 (ASCE 7-22) and the site-specific scaling factors in Eurocode 8. We demonstrate that these spectral floors are physically essential for flexible and isolated structures to constrain long-period kinetic energy, thereby mitigating the underestimation of residual drifts that fundamentally dictate repairability. Furthermore, this review explicitly aligns structural performance with the UN Sustainable Development Goals (SDG 9 & 11). By synthesizing advanced mitigation topologies with surrogate-assisted computational paradigms, this roadmap bridges the micro-to-macro scale gap between physical structural degradation and regional functional restoration, providing an actionable blueprint for sustainable urban networks. Full article

Distributed seismic sensor networks integrated into the Internet of Things (IoT) infrastructure enable continuous condition monitoring of large-scale engineering structures. During long-term operation, however, measurement channels are subject to sensitivity drift, increased noise, and pulse artifacts that statistically mimic real vibration events. Related deep-learning techniques for noisy and ill-posed inverse problems have demonstrated the value of combining principled physical priors with deep models. Although the application domain differs, the underlying methodological insight—that constrained, physics-aware feature mappings can stabilize learning under noisy and partially observed conditions—directly motivates the use of a parameterized quantum circuit as a nonlinear feature transformer in the present work, where Hilbert space mapping serves as an analogous structural prior for the latent representation. Three principal fault modes are considered in this work, corresponding to the dominant degradation mechanisms observed in long-term seismic instrumentation: sensor drift, increased noise, and sensor failure. Each fault mode produces a distinct signature in the windowed feature space; the proposed model is trained to discriminate between them based on the latent CNN-LSTM-VQC representation. We propose a hybrid quantum-inspired deep-learning model (QC-DL) for the detection and diagnosis of channel-degradation anomalies. The architecture combines a 1D-CNN+LSTM feature extractor with a parameterized variational quantum circuit (VQC) used as a nonlinear feature transformer. All quantum experiments were performed on the QPanda3 CPUQVM simulator. The data were split chronologically prior to windowing to avoid information leakage. On real-world labeled accelerometric data with four operating modes (normal/drift/high-noise/failure), the QC-DL model achieved a macro-averaged F1 score of approximately 0.69 and per-class AUC values in the range 0.88–0.99. The mean early-detection latency was 1.6 s versus 2.1 s for the CNN-LSTM baseline (~24% reduction). An ablation study against a parameter-matched classical MLP showed that the gain is modest and not solely attributable to additional nonlinearity. The reported p-values (p = 0.70, p = 0.29) do not establish statistical significance. The results support the feasibility of hybrid quantum-inspired deep learning for sensor-channel verification, while highlighting the need for evaluation on real NISQ hardware. This paper proposes a hybrid quantum-inspired approach for detecting and diagnosing such anomalies in the time series of distributed seismic networks. The architecture combines a classical temporal feature extraction module based on one-dimensional convolutional layers and a recurrent long short-term memory (LSTM) network, which generates a latent window representation of the signal, with a parameterized variational quantum circuit used as a nonlinear feature processor in a hybrid computational circuit. Experimental validation was performed on real-world labeled data with multiple sensor degradation modes. The evaluation was organized in a scoring framework aligned with autonomous operation through window ranking and threshold alarm generation. In the experiments, the proposed model provided a macro-averaged F1 score of approximately 0.69 and area under the receiver operating characteristic (AUC) curve values in the range of 0.88–0.99 across classes, outperforming baseline deep models. The average early detection latency was 1.6 s versus 2.1 s for the baseline recurrent model (a 24% reduction). An ablative comparison with a control model based on a classical multilayer perceptron of comparable dimension confirmed that the improvement is not limited to the addition of additional nonlinearity. The obtained results indicate the potential of quantum-supported deep learning for improving the reliability of long-term vibration monitoring and verifying the correctness of sensor channels in distributed seismic networks. Full article

Upstream oil and gas operations generate large volumes of multivariate data from seismic surveys, well logs, production sensor networks, and reservoir simulation models. Advances in machine learning, artificial intelligence, and other Industry 4.0 technologies are increasingly enabling data-driven applications across exploration, reservoir characterization, drilling optimization, and production forecasting. However, publicly available upstream datasets vary substantially in data modality, labeling strategy, machine learning compatibility, and benchmark maturity. To date, no standardized framework or taxonomy exists to guide dataset selection, benchmark design, or cross-study comparison. This study addresses that gap by proposing a structured, machine learning-centric taxonomy that organizes upstream datasets according to properties that are directly relevant to machine learning requirements. The proposed taxonomy provides a shared reference framework to support consistent dataset description, informed selection, and reproducible benchmarking in upstream machine learning research and applications. Full article

Accurate identification of karst caves from seismic data is crucial for carbonate reservoir characterization, as these caves often serve as primary hydrocarbon storage spaces and migration pathways. However, it remains challenging due to the highly nonlinear relationship between seismic waveforms and cave geometries, as well as the noise propagation in skip connections inherent to U-Net-based methods. To address these limitations, this paper proposes UKAN-CBAM, a novel 3D network that synergistically integrates Tokenized Kolmogorov–Arnold Network (Tok-KAN) modules and Convolutional Block Attention Modules (CBAM) within a U-shaped encoder–decoder architecture. Unlike U-Net, which relies on linear convolutional kernels, the Tok-KAN modules employ learnable spline-based activation functions to better capture the nonlinear relationships between seismic waveforms and cave geometries. Furthermore, CBAM embedded in each skip connection adaptively recalibrates features along the channel and spatial dimensions, thereby suppressing noise and sharpening cave boundaries. Trained on synthetic data and validated on physical modeling data from the Sichuan Basin and field data from the Tarim Basin, UKAN-CBAM consistently outperforms U-Net, ResUNet, UNet-CBAM, and coherence attributes across multiple evaluation metrics. The proposed network delineates caves with improved continuity and sharper boundaries while reducing false positives, demonstrating strong generalization capability. The results indicate that the synergistic design of KAN’s nonlinear modeling and CBAM’s attention mechanism effectively mitigates the limitations of traditional approaches for karst cave identification. Full article

The management of European mountain landscapes is increasingly threatened by rural abandonment and escalating environmental risks. This study investigates an innovative Stewardship–Renewable Energy Communities model for the Central Apennines, exploring how post-seismic public reconstruction can serve as a financial engine for territorial maintenance. Utilizing Open Data Sisma administrative records and Photovoltaic Geographical Information System irradiation metrics, this research assesses the solar potential of 18 municipalities within the Sibillini seismic crater. To ensure a reliable baseline, a Building Suitability Coefficient was introduced as a conservative proxy for the public reconstruction sector. Results indicate that the implementation of a distributed network of 6.5 MWp across 325 public nodes, with a specific yield of 1390 kWh/kWp on the entire area, could generate 9 GWh/year. This translates to approximately EUR 1.08 million in annual revenue from energy incentives and sharing. This economic surplus provides a Stewardship Capacity sufficient to fund the active maintenance of 789.77 hectares per year through Nature-Based Solutions, based on a regional rate of 1200 EUR/ha. The novelty of this study lies in bridging post-disaster energy policy with landscape resilience, demonstrating that distributed rooftop solar portfolios represent a non-invasive, self-funding mechanism. By leveraging the reconstructed public stock, mountain territories can transition from passive neglect to active, energy-backed stewardship, offering a reproducible template for high-value cultural landscapes. Full article

The recent seismic activity in southeastern Turkey in February 2023 again emphasized the critical need to promptly evaluate structural damage to assist in emergency response operations. This study introduces a comprehensive ensemble deep learning approach to structural damage classification following earthquake events, based on a dataset containing 13,270 high-resolution images with 15 different damage classes. Six different state-of-the-art convolutional neural network models (VGG16, ResNet50, InceptionV3, DenseNet121, EfficientNetB0, and MobileNetV2) are combined using a weighted voting approach to handle extreme class imbalance using weighted categorical cross-entropy loss. An integrated explainability component is incorporated into the trained convolutional neural network models to highlight the image regions that contribute to the predicted damage class, thereby improving the interpretability of deep learning decisions in safety-critical post-disaster assessment scenarios. The performance evaluation results show that the ensemble model achieves a test accuracy of 93.77%, with an increase of 2.67% compared to the best performing model individually. Notably, the ensemble model improves performance in minority classes like collapsed buildings. The proposed framework can be used to provide a powerful approach to structural damage evaluation, balancing accuracy with interpretability, to assist structural engineers in post-earthquake evaluation procedures. Full article

Automatic building extraction plays an important role in various remote sensing applications, such as seismic disaster investigation, seismic hazard risk assessment, urban planning, and photogrammetry. Despite the substantial progress, state-of-the-art building extraction methods are still limited by two issues: (i) existing approaches leverage convolutional layers or non-local self-attention to encode the position-aware dependencies, while they cannot flexibly adapt to the complex background contexts and varied structure patterns of buildings; and (ii) the local details cannot be well preserved by existing hierarchical decoders due to the imperfect feature aggregation, yielding unsatisfactory segmentation outputs in the local adjacent region. To address these issues, we propose Relative Position Aggregated Transformer (RPAFormer), which is capable of modeling the relative position dependencies of buildings and producing accurate local details using a dual attention transformer network. Specifically, we propose a Relative Position-aware Self-attention (RPSA) framework to learn the token dependencies within the local window. A transformer decoder network consisting of multiple Cross Masked Attention (CMA) blocks is also introduced to fuse the multi-scale features. Extensive experiments demonstrate the superior performance of the proposed method and its great promise for real-world engineering deployment. Full article

Wireless sensor networks (WSNs) deployed for seismic monitoring must sustain long-term operation under strict energy constraints, where premature node failure degrades spatial coverage and detection reliability. This paper presents a safety-constrained reinforcement learning framework for transmission scheduling in energy-harvesting seismic WSNs. The proposed approach integrates Proximal Policy Optimisation (PPO) with action masking and a runtime guard-layer safety filter that enforces battery-preservation and load-balancing constraints without retraining. The guard layer intercepts policy actions and substitutes safe alternatives when constraint violations are detected, using a scoring function that combines battery headroom with network-wide load equity. Experiments across three network scales (10, 15, and 30 nodes) with solar energy harvesting demonstrate that the guard-enhanced PPO achieves 99.46% transmission success at 30 nodes while maintaining 66.47% node survival—a 58.3% improvement in survival over the highest-reward baseline (Closest) at the cost of only a 6.2% reduction in cumulative reward. Crucially, the guard-enhanced policy outperforms the unconstrained PPO baseline simultaneously on cumulative reward ($+11.4\%$), transmission success ($+0.8$ pp), and node survival ($+15.4\%$), demonstrating that hard safety constraints, when properly aligned with the system’s energy model, provide both performance and safety gains rather than a fundamental trade-off. Sensitivity analysis across event rates ($p_{\mathrm{event}}=0.5$ and $0.9$) confirms that the guard layer’s advantage persists under both moderate and extreme monitoring conditions. Analysis across scales reveals distinct operational regimes: at 10 nodes, heuristic baselines are near-optimal; at 30 nodes, learned policies dominate, and safety filtering becomes critical for sustained operation. Full article