Search Results (58)

The Lacul fără nume landslide dam in the Vrancea Mountains (Romania) represents a unique example of a dynamic landslide dam system characterized by recurrent damming–breaching cycles. Through the combined use of remote sensing, field investigations, and historical reconstruction, eight such cycles were documented over a period of 49 years. To the best of current knowledge, this is one of the few documented landslide dams reported in the scientific literature that exhibits frequent damming–breaching episodes involving repeated dam failure, renewed slope instability, and subsequent re-damming with renewed lake impoundment over comparatively short timescales. The observed persistence and spatial extent of the associated lake are highly variable, ranging from 12 days to almost 10 years and from 23,920 m² to 82,610 m², respectively. Antecedent precipitation was frequently elevated prior to lake state transitions, but a seasonally constrained Monte Carlo analysis showed no significant departure from the climatic background, while numerous intense rainfall periods occurred without documented transitions. Similarly, no systematic temporal association was identified between recurrent lake state transitions and regional seismicity, although the initial dam formation coincided with the 1977 Mw 7.4 Vrancea earthquake. These findings suggest that precipitation conditions and seismicity alone cannot explain the recurrent damming and drainage, which likely result from interactions between hydrometeorological forcing, geomorphic processes, and human influences. Taken together, the results and the proposed conceptual model demonstrate that debris-flow-generated landslide dams can evolve into persistent and dynamic geomorphic systems capable of posing recurring hazards over multiple decades. Full article

Despite its geodynamic significance, the northern Tyrrhenian Back-Arc Basin, characterized by a complex tectono-magmatic evolution, remains poorly investigated. We present the first geophysical characterization of the Flavia Seamount, a previously uninvestigated edifice in the northern Tyrrhenian Sea, integrating new multibeam, seismic, and magnetic data. The new high-resolution bathymetric data reveal a flat, nearly circular summit and strongly asymmetric flanks. Reduced-to-the-pole magnetic anomalies exhibit a north–south polarity pattern, with positive values in the northern sector and negative values in the southern sector. Seismic data, integrated with Sparker profiles collected in 1985, reveal a flat-topped acoustic basement overlain by a ~100 m thick stratified sequence and affected by inactive east-dipping extensional faults, indicating tectonic control on the evolution of the seamount. Widespread landslide scarps and associated mass-transport deposits document recurrent gravitational instability along the flanks and within surrounding basins. Pockmark morphometry suggests distinct formation processes, with summit pockmarks controlled by fluid seepage and gravitational processes, and basin pockmarks mainly related to fluid escape from mass-transport deposits. Magnetic forward modelling constrained by seismic data provides new insights into the distribution of magnetic susceptibility bodies and the crustal architecture beneath the seamount. Results suggest that the present-day morphology of the Flavia Seamount reflects the combined effects of tectonic, sedimentary, and gravitational processes. Full article

The aim of this study is to determine the reliability level (the probability of failure-free operation) of a masonry building with load-bearing walls based on the conducted experimental investigations. The objective of the study is to compare the obtained reliability and failure risk values with the corresponding values calculated using the results of the structural certification. In 2017–2018, and subsequently in 2023–2024, a comprehensive structural certification of the multi-apartment residential building stock was carried out for the first time in the city of Almaty. A total of 1609 multi-story masonry buildings with heights of two to four stories were identified. Based on the certification results, quantitative estimates of the prior and posterior probabilities of failure and reliability for masonry buildings were obtained for the first time. The recurrence of earthquakes was taken into account. The novelty of the study lies in the experimental investigation of a three-story masonry building of series 308. The dynamic excitation was generated by an inertial vibration machine installed on the floor slab. As the inertial load increased, the resonant vibration period changed by a factor of three. This indicates that the building underwent significantly nonlinear deformation. The structure sustained substantial damage. Using statistical simulation methods based on the experimental data, the prior probabilities of failure for masonry buildings were calculated. In this case, the seismic action was modeled as a non-stationary random process with the deterministic envelope proposed by F. F. Aptikaev. Probabilistic estimates of the reliability of masonry buildings were obtained from the certification results both with and without taking into account the recurrence of earthquakes. The obtained estimates of reliability and failure probability can be used to develop practical recommendations aimed at reducing risk and expected losses in the event of possible earthquakes. It is recommended that masonry buildings with load-bearing brick walls either be structurally strengthened or be demolished. Full article

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

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

Accurate prediction of structural dynamic responses is critical for seismic analysis and decision-making throughout the structural life cycle. While model-driven and data-driven approaches have advanced practice, reliable prediction under limited data remains challenging due to the high cost of acquisition and simulation. This study proposes a Self-Attention-Enhanced Physics-Informed Gated Recurrent Unit network, SA-PhyGRU, for efficient and accurate seismic response prediction. The proposed network integrates GRU dynamics with a self-attention mechanism to capture long-range temporal dependencies and improve computational efficiency, while embedding physical constraints to enhance fidelity and generalization. Numerical and experimental validations on a three-story frame and a California hotel building show that SA-PhyGRU consistently outperforms conventional baselines in both accuracy and runtime, achieving improvements of up to 11.6% in $R^2,$ with pronounced gains in small-sample regimes. These results highlight SA-PhyGRU as an effective and generalizable approach for structural seismic response prediction and performance evaluation. Full article

This study evaluates the seismic performance and life-cycle economic implications of designing essential urban mid-rise reinforced concrete moment-resistant frame (MRF) buildings to maintain linear elastic behavior up to the Immediate Occupancy (IO) performance level. While most urban buildings are commonly designed to respond non-linearly in order to reduce initial construction costs, the current Mexico City Building Code (MCBC) permits that essential facilities, such as hospitals and schools, maintain linear behavior during moderate-to-strong earthquakes. This code establishes a maximum story drift ratio equal to 0.0075 for essential buildings constituted by MRF subjected to seismic events with a 250-year recurrence interval; in addition, it recommends ductile structural behavior to achieve Life Safety performance at a 450-year recurrence interval. Given the significant differences in occupancy, functionality, and contents of critical facilities, here it is analyzed whether the linear elastic design criterion is efficient for both secondary care hospitals and public schools. Two three-story and five-story MRF buildings, located on firm and transition soil, respectively, are analyzed. This study addresses the probability of brittle-type failure risk, the optimal allowable story drift at the IO performance level, the potential need for use-dependent drift limits, and the contribution of contents and nonstructural components to the total expected seismic losses. The seismic risk and economic performance are quantified through seismic hazard analysis, incremental dynamic analysis, fragility modeling, Monte Carlo simulation, and life-cycle cost evaluation. Full article

Different methodologies have been developed for the analysis and study of dynamical systems, including both theoretical models and natural systems. Examples span a wide range of applications, such as astronomy, financial and economic time series, biophysical systems, physiological phenomena, and Earth sciences, including seismicity and climatic processes. The study of these complex systems is commonly based on the analysis of the signals they generate, using mathematical tools to extract relevant information. A broad spectrum of mathematical disciplines converges in this context, including stochastic, probability and statistical theory, entropic and informational measures, fractal and multifractal analysis, natural time analysis, modeling of non-linearity and recurrence methods, generalized entropies, non-extensive systems, machine learning, and high-dimensional and multivariate complexity. Research in this area is largely focused on the characterization of complex systems, providing indicators of determinism or stochasticity, distinguishing between regularity, chaos, and noise, and identifying topological as well as disorder-regularity features. In addition, short- and long-term forecasting, together with the identification of short- and long-range correlations, play a central role in such characterization. To address these objectives, numerous mathematical tools have been developed for the analysis of time series and point processes, each designed to capture specific signal properties. In this work, many of the most important tools used in time series analysis are compiled and reviewed, highlighting their main characteristics and the different types of complex systems to which they have been applied. Full article

Lightweight aggregate concrete (LWAC) is a practical alternative to conventional concrete in civil engineering, offering advantages such as reduced density, enhanced insulation properties, and improved seismic performance. However, segregation during compaction remains a limitation, as it can lead to non-uniform material distribution and reduced compressive strength. This study addresses this issue by combining non-destructive techniques with deep learning methods to predict the compressive strength of LWAC. We propose an explainable approach based on a convolutional recurrent neural network architecture, enhanced by unsupervised clustering and SHapley Additive exPlanations (SHAP), to improve interpretability. To optimize predictive performance, several aggregation strategies are evaluated at the recurrent layer before the dense layers, including full-sequence flattening, max pooling, average pooling, and an attention mechanism over the full sequence. Experimental results show that the proposed model outperforms conventional machine learning methods such as multilayer perceptron (MLP), random forest (RF), and support vector regression (SVR), as well as ensemble methods such as gradient boosting (GBR), XGBoost, and weighted average ensemble (WAE). Furthermore, when combined with unsupervised clustering, the model identifies latent behavioral patterns that are not observable through traditional evaluation techniques. This demonstrates the potential of integrating non-destructive testing with interpretable deep learning as a reliable approach for the structural assessment of LWAC. Full article

Forecasting post-seismic landslide displacement is challenged by the difficulty in distinguishing short-term acceleration from creep and the risk of spatiotemporal leakage. To address this, an interpretable deep-learning framework is developed, integrating SBAS-InSAR time series with an Attention-enhanced Gated Recurrent Unit (Attention-GRU). Prior to modeling, a multi-stage preprocessing strategy, including empirical mode decomposition, is applied to mitigate noise and delineate active deformation zones. Unlike standard architectures, the model’s temporal attention mechanism adaptively amplifies critical precursory acceleration phases. Furthermore, a strict landslide-object-based partitioning strategy is employed to rigorously mitigate spatiotemporal leakage. The framework was evaluated in the Le’an Town landslide cluster using multi-source data. Targeting identified hazardous regions, the method achieved an R² of 0.93 and reduced MAPE by 42.7% relative to the SVR baseline. This reflects a location-specific predictive capability, within active zones rather than regional generalization. SHapley Additive exPlanations (SHAP) further confirmed the model captures physical relationships, such as sensitivity to 25–35° slopes and vegetation degradation. Ultimately, the proposed framework offers a transparent, physically interpretable tool for operational hazard mitigation. Full article

This study presents an improved Nishenko–Buland (NB) model to address systematic biases in estimating the coefficient of variation for earthquake recurrence intervals based on a normalizing function ${\displaystyle \frac{\mathrm{T}}{{\mathrm{T}}_{\mathrm{a}\mathrm{v}\mathrm{e}}}}$. Through Monte Carlo simulations, we demonstrate that traditional NB methods significantly underestimate the coefficient of variation when applied to limited paleoseismic datasets, with deviations reaching between 30 and 40% for small sample sizes. We developed a linear transformation and iterative optimization approach that corrects these statistical biases by standardizing recurrence interval data from different sample sizes to conform to a common standardized distribution. Application to 26 fault segments across 15 major active faults in the Hetao graben system yields a corrected coefficient of variation of α = 0.381, representing a 24% increase over the traditional method (α₀ = 0.307). This correction demonstrates that conventional approaches systematically underestimate earthquake recurrence variability, potentially compromising seismic hazard assessments. The improved model successfully eliminates sampling bias through iterative convergence, providing more reliable parameters for probability distributions in renewal-based earthquake forecasting. Full article

The Nezamabad Fault System (NFS) in the Fars area of the Zagros Fold–Thrust Belt represents a persistent, basement-rooted transverse shear zone that fundamentally controls the regional hydrocarbon system. This study integrates seismicity distribution, isopach analysis, and tectono-stratigraphic modeling from the Triassic to the Cenozoic to unravel how recurrent basement reactivation governs trap evolution. Isopach maps reveal a pronounced southwest-thickening asymmetry, with Triassic successions exceeding 1400 m, indicating long-term differential subsidence during four key phases: (1) Triassic syn-rift salt accumulation (Dashtak Formation) forming the primary detachment; (2) Jurassic–Early Cretaceous passive subsidence promoting source rock deposition; (3) Mid-Cretaceous transpression enhancing reservoir dolomitization; and (4) Late Cretaceous–Cenozoic inversion generating hybrid traps. Seismicity analysis of over 240 events confirms the 256-km-long NFS is a crustal-scale structure, with most foci at 10–33 km depth and others extending to 150 km, implying lithospheric stress transfer. This deep-crustal activity has periodically reorganized stress, enhanced fracture permeability, and rejuvenated traps through seismic pumping and cross-scale mechanical coupling. The results demonstrate that hydrocarbons in the Fars area are not a passive outcome of folding but a dynamic expression of lithospheric coupling. The findings establish a predictive framework for identifying analogous basement-influenced petroleum systems in other foreland fold–thrust belts worldwide. Full article

The sonic log is a key tool for assessing the mechanical properties of rocks, identifying structural features, calibrating seismic data, and monitoring well integrity. However, sonic data are often incomplete due to time and cost constraints, tool failures, or unreliable measurements. Traditional approaches to generate synthetic sonic logs usually rely on empirical relationships or statistical methods. In this study, we applied an artificial intelligence approach in which a deep neural network was trained with real data from an oilfield in Mexico to reconstruct sonic logs based on their relationships with other geophysical well logs. Three models, each using different input logs, were trained to predict the sonic response. The models were validated on wells excluded from training, and performance was evaluated using the root mean square error (RMSE) and mean absolute percentage error (MAPE), showing satisfactory accuracy. The models achieved RMSE values between 1.4 and 1.7 [$\mathsf{\mu }$s/ft] and MAPE values between 2.1 and 2.6% on independent test wells, confirming robust predictive performance. We also generated synthetic sonic logs for wells where no sonic data were originally acquired, demonstrating the practical value of the proposed method. This work integrates convolutional (CNN) and recurrent (GRU) layers in a single deep-learning architecture, trained under strict well-level validation. The workflow is demonstrated on wells from the Tabasco Basin, representing a field-scale deployment not previously reported in similar studies. Full article

This study combines a multidisciplinary approach to Pyrenean and Alpine glacial lakes to characterize the sensitivity of Late Glacial to Holocene subaquatic flood deposits in deltaic environments to slope failures triggered either by earthquakes, rockfalls, or snow avalanches. To clarify the possible interactions between environmental changes and these natural hazards in mountain and piedmont lakes, we analyze the lacustrine sedimentary records of key historical events and discuss the recurrence of similar regional events in the past. High-resolution seismic profiles and sediment cores from large perialpine lakes (Bourget, Geneva, and Constance) and from small mountain lakes in the French Alps and the Pyrenees were used to establish a conceptual model linking environmental changes, tributary flood sedimentary processes, subaquatic deltaic depocenters, and potentially tsunamigenic mass-wasting deposits. These findings illustrate the specific signatures of the largest French earthquakes in 1660 CE (northern Pyrenees) and in 1822 CE (western Alps) and suggest their recurrence during the Holocene. In addition, the regional record in the Aiguilles Rouges massif near Mont Blanc of the tsunamigenic 1584 CE Aigle earthquake in Lake Geneva may be used to better document a similar Celtic event ca. 2300 Cal BP at the border between Switzerland and France. Full article

Landslides represent a recurrent hazard in tropical mountain environments, where rapid urbanization and extreme rainfall amplify disaster risk. The Sentani region of Papua, Indonesia, is highly vulnerable, as demonstrated by the catastrophic debris flows of March 2019 that caused fatalities and widespread losses. This study developed high-resolution landslide susceptibility maps for Sentani using an ensemble machine learning framework. Three base learners—Random Forest, eXtreme Gradient Boosting (XGBoost), and CatBoost—were combined through a logistic regression meta-learner. Predictor redundancy was controlled using Pearson correlation and Variance Inflation Factor/Tolerance (VIF/TOL). The landslide inventory was constructed from multitemporal satellite imagery, integrating geological, topographic, hydrological, environmental, and seismic factors. Results showed that lithology, Slope Length and Steepness Factor (LS Factor), and earthquake density consistently dominated model predictions. The ensemble achieved the most balanced predictive performance, Area Under the Curve (AUC) > 0.96, and generated susceptibility maps that aligned closely with observed landslide occurrences. SHapley Additive Explanations (SHAP) analyses provided transparent, case-specific insights into the directional influence of key factors. Collectively, the findings highlight both the robustness and interpretability of ensemble learning for landslide susceptibility mapping, offering actionable evidence to support disaster preparedness, land-use planning, and sustainable development in Papua. Full article