Search Results (146)

With the continuous advancement of deep learning algorithms and the rapid growth of computational resources, deep learning technology has undergone numerous milestone developments, evolving from simple BP neural networks into more complex and powerful network models such as CNNs, LSTMs, RNNs, and GANs. In recent years, the application of deep learning technology in ionospheric modeling has achieved breakthrough advancements, significantly impacting navigation, communication, and space weather forecasting. Nevertheless, due to limitations in observational networks and the dynamic complexity of the ionosphere, deep learning-based ionospheric models still face challenges in terms of accuracy, resolution, and interpretability. This paper systematically reviews the development of deep learning applications in ionospheric modeling, summarizing findings that demonstrate how integrating multi-source data and employing multi-model ensemble strategies has substantially improved the stability of spatiotemporal predictions, especially in handling complex space weather events. Additionally, this study explores the potential of deep learning in ionospheric modeling for the early warning of geological hazards such as earthquakes, volcanic eruptions, and tsunamis, offering new insights for constructing ionospheric-geological activity warning models. Looking ahead, research will focus on developing hybrid models that integrate physical modeling with deep learning, exploring adaptive learning algorithms and multi-modal data fusion techniques to enhance long-term predictive capabilities, particularly in addressing the impact of climate change on the ionosphere. Overall, deep learning provides a powerful tool for ionospheric modeling and indicates promising prospects for its application in early warning systems and future research. Full article

This article introduces a novel smart deployable scissor lift brace system designed to mitigate earthquake risks in buildings prone to the soft-story effect. The system addresses the limitations of traditional retrofitting methods, providing an efficient solution for enhancing the structural integrity of buildings while preserving the functionality of open lower floors, commonly used for car parking or retail spaces. The soft-story effect, characterized by a sudden reduction in lateral stiffness in one or more levels of a building, often leads to catastrophic collapses during large earthquakes, resulting in significant structural damage and loss of life. The proposed system is triggered by signals from the Earthquake Early Warning (EEW) system, advanced technologies capable of detecting and broadcasting earthquake alerts within seconds which are currently implemented in countries and regions such as Japan, parts of the USA, and parts of Europe. The smart deployable system functions by instantly activating upon receiving EEW signals. Unlike traditional retrofitting approaches, such as adding braces or infill walls, which compromise the open layout of lower floors, this innovative device deploys dynamically during seismic events to enhance the building’s stiffness and lateral stability. The article demonstrates the system’s functionality through a conceptual framework supported by proof-of-concept experiments. Historical earthquake time histories are simulated to test its effectiveness. The results reveal that the system significantly improves the stiffness of the structure, reducing displacement responses during events of seismic activity. If properly proportioned and optimized, this system has the potential for widespread commercialization as a seismic risk mitigation solution for buildings vulnerable to the soft-story effect. Full article

As large-scale depletion of shallow coal seams and increasing mining depths intensify, the frequency and intensity of mining-induced earthquake events have significantly risen. Due to the complex formation mechanisms of high-energy mining-induced earthquakes, precise identification and early warning cannot be achieved with a single monitoring method, posing severe challenges to coal mine safety. Therefore, this study conducts an in-depth risk analysis of two high-energy mining-induced earthquake events at the 3308 working face of Yangcheng Coal Mine, integrating microseismic monitoring, stress monitoring, and seismic source mechanism analysis. The results show that, by combining microseismic monitoring, seismic source mechanism inversion, and dynamic stress analysis, critical disaster-inducing factors such as fault activation, high-stress concentration zones, and remnant coal pillars were successfully identified, further revealing the roles these factors play in triggering mining-induced earthquakes. Through multi-dimensional data integration, especially the effective detection of the microseismic “silent period” as a key precursor signal before high-energy mining-induced earthquake events, a critical basis for early warning is provided. Additionally, by analyzing the spatiotemporal distribution patterns of different risk factors, high-risk areas within the mining region were identified and delineated, laying a foundation for formulating precise prevention and control strategies. The findings of this study are of significant importance for mining-induced earthquake risk management, providing effective assurance for safe production in coal mines and other mining environments with high seismic risks. The proposed analysis methods and control strategies also offer valuable insights for seismic risk management in other mining industries, ensuring safe operations and minimizing potential losses. Full article

The available historical documents for the city of Alexandria indicate that it was damaged to varying degrees by several (historical and instrumentally recorded) earthquakes and by highly destructive tsunamis reported at some places along the Mediterranean coast. In this work, we applied the neo-deterministic seismic hazard analysis (NDSHA) approach to the Alexandria metropolitan area, estimating ground motion intensity parameters, e.g., peak ground displacement (PGD), peak ground velocity (PGV), peak ground acceleration (PGA), and spectral response, at selected rock sites. The results of this NDSHA zonation at a subregional/urban scale, which can be directly used as seismic input for engineering analysis, indicate a relatively high seismic hazard in the Alexandria region (e.g., 0.15 g), and they can provide an essential knowledge base for detailed and comprehensive seismic microzonation studies at an urban scale. Additionally, we established detailed tsunami hazard inundation maps for Alexandria Governorate based on empirical relations and considering various Manning’s Roughness Coefficients. Across all the considered scenarios, the average estimated time of arrival (ETA) of tsunami waves for Alexandria was 75–80 min. According to this study, the most affected sites in Alexandria are those belonging to the districts of Al Gomrok and Al Montazah. The west of the city, called Al Sahel Al Shamally, is less affected than the east, as it is protected by a carbonate ridge parallel to the coastline. Finally, we emphasize the direct applicability of our study to urban planning and risk management in Alexandria. Our study can contribute to identifying vulnerable areas, prioritizing mitigation measures, informing land-use planning and building codes, and enhancing multi-hazard risk analysis and early warning systems. Full article

Magnitude prediction is a key focus in earthquake science research, and using machine learning models to analyze seismic data, identify pre-seismic anomalies, and improve prediction accuracy is of great scientific and practical significance. Taking the southern part of China’s North–South Seismic Belt (20° N~30° N, 96° E~106° E), where strong earthquakes frequently occur, as an example, we used the sliding time window method to calculate 11 seismicity indicators from the earthquake catalog data as the characteristic parameters of the training model, and compared six machine learning models, including the random forest (RF) and long short-term memory (LSTM) models, to select the best-performing LSTM model for predicting the maximum magnitude of an earthquake in the study area in the coming year. The experimental results show that the LSTM model performs exceptionally well in predicting earthquakes of magnitude 5 < ML ≤ 6 within the time window of the test set, with a prediction success rate of 85%. Additionally, the study explores how different time windows, spatial locations, and parameter choices affect model performance. It found that longer time windows and key seismicity parameters, such as the b-value and the square root of total seismic energy, are crucial for improving prediction accuracy. Finally, we propose a magnitude interval-based assessment method to better predict the actual impacts that different magnitudes may cause. This method demonstrates the LSTM model’s potential in predicting moderate to strong earthquakes and offers new approaches for earthquake early warning and disaster mitigation. Full article

The present work explores the use of geophysical surveys as valuable tools for the study and sustainable management of landslides, with a particular focus on Ecuador. As an Andean country, Ecuador’s geomorphology and geology are dominated by volcano-sedimentary materials and processes, which confers a high susceptibility to landslides. In the last few years, a number of landslide events (such as those at La Josefina, Alausí, and Chunchi) have given rise to disasters with significant material damage and loss of life. Climatic events, affected by climate change, earthquakes, and human activity, are the main landslide triggers. Geophysical surveys, like seismic refraction, electrical resistivity tomography (ERT), and ground-penetrating radar (GPR), are easy and low-cost techniques that provide valuable and critical subsurface data. They can help define the failure surface, delimit the mobilized materials, describe the internal structure, and identify the hydrological and geotechnical parameters that complement any direct survey (like boreholes and laboratory tests). As a result, they can be used in assessing landslide susceptibility and integrated into early warning systems, mapping, and zoning. Some case examples of large landslide events in Ecuador (historical and recent) are analyzed, showing how geophysical surveys can be a valuable tool to monitor landslides, mitigate their effects, and/or develop solutions. Combined or isolated geophysical techniques foster sustainable management, improve hazard characterization, help protect the most vulnerable regions, promote community awareness for greater safety and resilience against landslides, and support governmental actions and policies. Full article

To enhance real-time S-wave detection in the railway earthquake early warning (EEW) system, we improved the existing short-term average/long-term average (STA/LTA) algorithm. This enhancement focused on developing a more robust and computationally efficient method. Specifically, we introduced noise reflecting P-wave amplitude information before the P-wave to better distinguish between P- and S-waves. By applying this modified STA/LTA method, we achieved a significant improvement in S-wave detection accuracy. For seismic waveforms from stations located within 100 km of the epicenter of each earthquake, with magnitude of M5.5–6.5 and depths ≤ 100 km, the detection accuracy within 1.5 s of the correct time (manual picking) was 81.0%, compared to the 49.0% accuracy of the currently operational railway EEW system. Importantly, despite the improved accuracy, the computational cost of the new method remains comparable to the existing system, allowing for easy integration into the operational EEW system. This development is crucial for preventing false alarms, especially moderate earthquakes (~M6) because issuing warn-ings in unnecessary areas can have a significant social impact. Future plans involve implementing this method into the current system to further improve early warning capabilities and minimize false alarms. Full article

To improve the performance of earthquake early warning (EEW) systems, we propose an approach that utilizes the time-dependence of P-wave displacements to estimate the earthquake magnitude (M) based on the relationship between M and the displacement. The traditional seismological understanding posits that this relationship achieves statistical significance when the displacement reaches its final peak value, resulting in the adoption of time-constant coefficients. However, considering the potential for earlier establishment of the relationship’s significance than conventionally assumed, we analyze waveforms observed in Japan and determine the intercept in the relationship as a function of time from the P-wave onset. We demonstrate that our approach reduces the underestimation of M in the initial P-wave stages compared to the conventional technique. Consequently, we find a significant rise in the number of earlier warnings in the Japanese railway EEW system. Due to the inherent trade-off between the immediacy and accuracy of alarm outputs, the proposed method unavoidably leads to an increase in the frequency of alerts. Nonetheless, if deemed acceptable by system users, our approach can contribute to EEW performance improvement. Full article

Since the Wenchuan earthquake in 2008, Old Beichuan County-town has experienced significant subsidence due to the disruption of the geological environment and the concurrent increase in precipitation. The ongoing land surface deformation poses a threat to the preservation and utilization of the Beichuan National Earthquake Ruins Museum (BNERM), as well as to the safety of urban residents’ lives. However, the evolutionary characteristics of surface deformation in these areas remain largely unexplored. Here, we focused on the BNERM control zone and employed the small-baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technique to accurately measure land surface deformation and its spatiotemporal changes. Subsequently, we integrated this data with land cover types and precipitation to investigate the driving factors of deformation. The results indicate a slight overall elevation increase in the study area from June 2015 to May 2023, with deformation rates varying between −35.2 mm/year and 22.9 mm/year. Additionally, four unstable slopes were identified within the BNERM control zone. Our analysis indicates that surface deformation in the study area is closely linked to changes in land cover types and precipitation, exhibiting a seasonal cumulative pattern, and active geological activity may also be a cause of deformation. This study provides invaluable insights into the surface deformation characteristics of the BNERM and can serve as a scientific foundation for the protection of earthquake ruins, risk assessment, early warning, and disaster prevention measures. Full article

Tsunamis are devastating natural phenomena that cause extensive damage to both human life and infrastructure. To mitigate such impacts, tsunami early warning systems have been deployed globally. South Korea has also initiated a project to install a tsunami warning system to monitor its surrounding seas. To ensure reliable warning decisions, various types of data must be combined, but efficiently transmitting heterogeneous data poses a challenge due to the unique characteristics of underwater acoustic communication. Therefore, this paper proposes a Hybrid Duplex Medium Access Control (HDMAC) protocol designed for a tsunami warning system, with a specific focus on heterogeneous data transmission. HDMAC efficiently handles both seismic and environmental data by utilizing hybrid duplexing, which combines frequency duplex for seismic data with time duplex for environmental data. The protocol addresses the distinct transmission requirements for each data type by optimizing channel utilization through a group Automatic Repeat request (ARQ) scheme and packet size adjustment. Theoretical analysis predicts that HDMAC can achieve a channel utilization of up to 0.91 in smaller networks and 0.64 in larger networks. HDMAC is validated through simulations, and the simulation results closely match these predictions. The simulation results demonstrate the efficiency of HDMAC in supporting real-time submarine earthquake monitoring systems. Full article

Earthquake-induced landslides (EQILs) represent a serious secondary disaster of earthquakes, and conducting an effective assessment of earthquake-induced landslide susceptibility (ELSA) post-earthquake is helpful in reducing risk. In light of the diverse demands for ELSA across different time periods following an earthquake and the growing availability of data, this paper proposes using remote sensing data to dynamically update the ELSA model. By studying the Ms 6.2 earthquake in Jishishan County, Gansu Province, China, on 18 December 2023, rapid assessment results were derived from 12 pre-trained ELSA models combined with the spatial distribution of historical earthquake-related landslides immediately after the earthquake for early warning. Throughout the entire emergency response stage, the ELSA model was dynamically updated by integrating the EQILs points interpreted from remote sensing images as new training data to enhance assessment accuracy. After the emergency phase, the remote sensing interpretation results were compiled to create the new EQILs inventory. A high landslide potential area was identified using a re-trained model based on the updated inventory, offering a valuable reference for risk management during the recovery phase. The study highlights the importance of integrating remote sensing into ELSA model updates and recommends utilizing time-dependent remote sensing data for sampling to enhance the effectiveness of ELSA. Full article

Shear-dominated hazards, such as induced earthquakes, pose an escalating threat to the sustainability and safety of the geothermal exploitation. Variations in fault orientations and compression–shear stress ratios exert a profound influence on the failure processes underlying these disasters. To better understand these effects on the shear failure mechanisms of hot dry rocks, mode-II fracturing tests on granites were conducted at varying loading angles (specifically, 55°, 60°, 65°, and 70°). These tests were accompanied by a comprehensive analysis of the mechanical properties, energy dissipation behavior, acoustic emission (AE) responses, and digital image correlation (DIC)-extracted displacement fields. The tensile–shear properties of stress-induced microcracks were discerned via AE characteristic parameter analysis and DIC displacement decomposition, and the mode-II fracture energy release rate was quantitatively characterized. The results reveal that with increasing compression–shear loading angles, the mechanical properties of granites are weakened, and the elastic strain energy at peak stress gradually decreases, while the slip-related dissipated energy increases. Throughout the fracturing process, the AE count progressively climbs and reaches a peak near catastrophic failure, with an upsurge in low-frequency and high-amplitude AE events. Microcrack distribution concentrates aggregation along the shear plane, reflecting the emergent displacement discontinuities evident in DIC contours. Both the AE characteristic parameter analysis and DIC displacement decomposition demonstrate that shear-sliding constitutes the paramount mechanism, and the fraction of shear-oriented microcracks and the ratio of tangential versus normal displacement escalate with increases in shear stress. This analysis is supported by the heightened propensity for transgranular microcracking events observed through scanning electron microscopy. As the shear-to-compression stress increases, the energy concentration along the shear band intensifies, with the gradient of the fitting line between cumulative AE energy and slip displacement steepening, indicative of a heightened mode-II energy release rate. These results contribute to a deeper understanding of the mode-II fracture mechanism of rocks, thereby providing a foundational basis for early warnings of shear-dominant geomechanical disasters, and improving the safety and sustainability of subsurface rock engineering. Full article

The objective of this study is to present a method for developing fragility curves for soil liquefaction that align with seismic hazards using Monte Carlo simulation. This approach can incorporate all uncertainties and variabilities in the input parameters. The seismic parameters, including earthquake magnitude (M) and associated peak ground acceleration (PGA), are jointly considered for the liquefaction assessment. The liquefaction potential and the resulting damages obtained by this method are more realistic. A case study is conducted using data from a sand-boil site in Yuanlin, Changhua County, where liquefaction occurred during the 1999 Chi-Chi earthquake in Taiwan. The findings indicate that the liquefaction potential index, I_L, the post-liquefaction settlement, S_t, and the liquefaction probability index, P_W, are all appropriate parameters for assessing liquefaction damages. The fragility curves for soil liquefaction developed through this method can support the performance-based earthquake engineering (PBEE) approach, provide guidance for liquefaction evaluation to the Taiwan Earthquake Loss Estimation System (TELES), and serve as a foundation for scenario simulation and an earthquake early warning system for liquefaction damages. Full article

This study explores the transformative potential of artificial intelligence (AI) in revolutionizing earthquake risk mitigation across six key areas. Unlike traditional approaches, this paper examines how AI-driven innovations can uniquely enhance early warning systems, enabling real-time structural health monitoring, and providing dynamic, multi-hazard risk assessments that seamlessly integrate seismic data with other natural hazards such as tsunamis and landslides. It introduces groundbreaking applications of AI in earthquake-resilient design, where generative design algorithms and predictive analytics create structures that optimally balance safety, cost, and sustainability. The study also presents a novel discussion on the ethical implications of AI in this domain, stressing the critical need for transparency, accountability, and bias mitigation. Looking forward, the manuscript envisions the development of advanced AI platforms capable of delivering real-time, personalized risk assessments, immersive public training programs, and collaborative design tools that adapt to evolving seismic data. These innovations promise not only to significantly enhance current earthquake preparedness but also to pave the way toward a future where the societal impact of earthquakes is drastically reduced. This work underscores the potential of AI’s role in shaping a safer, more resilient future, emphasizing the importance of continued innovation, ethical governance, and collaborative efforts. Full article

We introduce a novel LoRa-based multi-hop communication architecture as an alternative to the public internet for earthquake early warning (EEW). We examine its effectiveness in generating a meaningful warning window for the New Zealand-based decentralised EEW sensor network implemented by the CRISiSLab operating with the adapted Propagation of Local Undamped Motion (PLUM)-based earthquake detection and node-level data processing. LoRa, popular for low-power, long-range applications, has the disadvantage of long transmission time for time-critical tasks like EEW. Our network overcomes this limitation by broadcasting EEWs via multiple short hops with a low spreading factor (SF). The network includes end nodes that generate warnings and relay nodes that broadcast them. Benchmarking with simulations against CRISiSLab’s EEW system performance with internet connectivity shows that an SF of 8 can disseminate warnings across all the sensors in a 30 km urban area within 2.4 s. This approach is also resilient, with the availability of multiple routes for a message to travel. Our LoRa-based system achieves a 1–6 s warning window, slightly behind the 1.5–6.75 s of the internet-based performance of CRISiSLab’s system. Nevertheless, our novel network is effective for timely mental preparation, simple protective actions, and automation. Experiments with Lilygo LoRa32 prototype devices are presented as a practical demonstration. Full article