Search Results (81)

The Marmara region of Türkiye, situated along the North Anatolian Fault Zone (NAFZ), constitutes one of the most seismically active and densely monitored zones globally. Given the region’s high vulnerability and the catastrophic impacts of historical events—notably the 1999 İzmit and 2023 Kahramanmara¸s sequences—there is a critical need for advanced seismic hazard risk assessment (SHRA) methods that move beyond static models. This review examines the paradigm shift from traditional geophysics to big data seismology, characterized by the “Five Vs”: volume, velocity, variety, veracity, and value. Critically, we distinguish between two fundamentally different problems: Earthquake Early Warning (EEW), which operates on sub-second timescales after rupture initiation, and probabilistic earthquake forecasting, which operates on timescales of years to decades. The study discusses how cloud-native platforms such as Azure Databricks, combined with data pipelines using Apache Kafka (version 3.5.1) and Apache Spark (version 4.1.2), enable the real-time processing of petabyte-scale seismic sensor streams. Key technological tools, including Physics-Informed Neural Networks (PINNs) and deep learning models such as PhaseNet, are analyzed for their demonstrated ability to enhance EEW systems through sub-second phase picking and automated event detection. Seismic tomography is also undergoing AI-enabled transformation, yielding higher-resolution subsurface imaging. We present statistical validation metrics and uncertainty quantification methods essential for credible hazard assessment. By addressing computational bottlenecks through hybrid computing architectures and edge computing, this framework aims to improve the warning lead time for Istanbul’s critical infrastructure. This work provides a structured roadmap for bridging the gap between traditional seismic data analysis and operational predictive analytics in the Marmara region. Full article

Effective emergency relief allocation in dynamic post-disaster environments depends critically on accurate and timely demand information. From a sustainability perspective, improving allocation accuracy is essential for using scarce rescue resources efficiently and supporting resilient disaster response. However, existing demand forecasting approaches frequently exhibit systematic bias, leading to resource misallocation and diminished rescue outcomes. Although deploying on-site assessment teams can partially mitigate this limitation, a unified framework that systematically embeds field assessment feedback into operational allocation processes remains lacking. To bridge this gap, this study proposes a multi-agent joint assessment-allocation model that facilitates coordinated operations between demand assessment and resource distribution activities. The sequential decision-making process is formulated as a Markov Decision Process (MDP), and deep reinforcement learning is employed to coordinate the actions of assessment and allocation teams, enabling allocation policies to be continuously refined through real-time field feedback. By improving the match between actual demand and material supply, the proposed model aims to support more resource-efficient disaster response under demand uncertainty. An empirical case study based on the 2025 Dingri County earthquake in Tibet is conducted to validate the proposed framework. Results demonstrate that integrating assessment feedback substantially improves resource allocation performance: in multi-site rescue scenarios, the framework increases the number of rescued individuals, reduces mission completion time, and enhances overall demand satisfaction. Further sensitivity analysis reveals that a moderate increase in team size strengthens cross-site coordination, whereas excessive team deployment yields diminishing returns and may generate operational redundancy. These findings suggest that sustainable emergency management depends not only on the availability of relief resources, but also on the efficient coordination of real-time information acquisition and material allocation. The proposed framework offers a generalizable approach for integrating real-time information acquisition with dynamic relief allocation. It improves the efficient utilization of scarce rescue resources, reduces avoidable operational redundancy, and strengthens the resilience of emergency response systems, thereby contributing to sustainable disaster risk reduction. Full article

Reliable forecasting of seismic activity trends is essential for regional seismic hazard analysis. Based on earthquake catalogs from 1500 to 2026, this study investigates the spatiotemporal evolution of seismic activity in the North-South Seismic Belt using a hybrid framework that integrates Wiener filtering and artificial neural networks. Seismic activity is modeled as a discrete-time stochastic process, and a time series of earthquakes with magnitudes ≥ 6.0 is constructed. Wiener filtering is applied to establish an optimal linear relationship between input and output under the minimum mean square error criterion, and multi-origin extrapolation is employed to predict earthquakes with magnitudes ≥ 7.0 over the next century. The results reveal several stable peaks or peak clusters that agree well with historical strong earthquakes, with prediction errors generally within approximately three years. Sensitivity analyses indicate that longer time series (∼500 years) and higher threshold magnitudes (≥6.0) enhance prediction stability, although the method shows limitations in spatial prediction. To address this issue, a 16–8–4 artificial neural network model is developed, and seismic sequence features are extracted using a sliding time window approach to perform both temporal and spatial forecasting. The artificial neural network achieves high accuracy in temporal prediction (maximum error ≈ 0.5) and outperforms Wiener filtering in spatial prediction, capturing the migration characteristics of seismic activity. The results further suggest that earthquakes with magnitudes ≥ 7.0 are more likely to occur within the latitude range of 30.5–33.0° N in the near future. Full article

The nonlinearity and the extreme variability of seismic signals makes the detection of earthquakes difficult. Although the conventional deep-learning models can be used to extract useful features, they cannot be used in early-warning systems due to their non-interpretability. In this study, a Bidirectional Long Short-Memory network with an attention system (Bi-LSTM-Attn) is proposed to detect seismic events using the ConvNetQuake dataset. To improve the quality of data, the entire preprocessing pipeline, such as signal filtering, segmentation, normalization, and noise reduction is employed. The model was optimized using hyperparameter tuning of sequence length, learning rates, and attention weighting to achieve the best number of true-positive detections and a minimum number of false alarms. The accuracy, precision and recall, F1-score, MSE, and ROC curves were used to assess the performance and the attention weight visualization allowed interpreting the model. It is proven through experiments that the Bi-LSTM-Attn model provides more credible and comprehensible forecasting in relation to baseline LSTM and GRU models. Making the high-accuracy classification and the transparent decision behavior, the approach will increase the level of trust to the automated seismic surveillance, as well as help to build the reliable global networks of earthquake early-warnings. Full article

Significant earthquakes can cause widespread infrastructure damage, social implications, and substantial economic losses. To mitigate these impacts, earthquake forecasting models have been developed to estimate earthquake occurrences and improve recovery efforts, with the Epidemic-Type Aftershock Sequence (ETAS) model being the most informative statistical framework for characterizing earthquake sequences. In this study, the ETAS model is used to estimate the model parameters for seismicity in Canada using the historical earthquake catalogue and to forecast long-term seismicity for seven different regions in Canada. Furthermore, the model is used to generate synthetic earthquake catalogues in order to assess its ability to replicate observed seismic patterns. The study identifies the southwestern region of Canada, associated with the coastal area of British Columbia, as being at the highest seismic risk, with a 66% exceedance probability for M7.5 events or above to occur in 30 years. In contrast, Alberta features the least seismic risk, with a 4% exceedance probability for events above 6.5 magnitude. For southeastern Canada, associated with Eastern Ontario and Southern Quebec, an exceedance probability of 74% for events above 6.0 magnitude poses the potential for significant damage due to the larger exposed population. Moreover, the resulting seismicity maps show the model’s capability for real-events analysis, but improvements are needed for further applications. 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

Predicting future seismic trends and occurrence of earthquakes remains a long-standing challenge in seismology. Despite substantial efforts to unravel the physical mechanisms underlying earthquake occurrence, currently, no well-defined physical or statistical model is capable of reliably predicting major earthquakes. However, machine learning methods have demonstrated exceptional proficiency in identifying patterns within large-scale datasets, offering a promising avenue for enhancing earthquake prediction performance. Within the framework of machine learning, this study has developed a feature extraction method based on seismic prediction zoning, improving the effectiveness of machine learning-based earthquake prediction. The research findings indicate that the ensemble learning Stacking method, which is based on seismic prediction zoning, exhibits superior performance and high robustness in predicting the annual maximum earthquake magnitude. Additionally, the long short-term memory (LSTM) method demonstrates commendable performance within specific tectonic zones (e.g., the southwestern Yunnan region), providing valuable guidance for analyzing seismic trends in these regions. Full article

The b-value is a critical parameter for gauging seismic activity and is essential for seismic hazard assessment, monitoring stress evolution in focal zones, and forecasting major earthquakes. The minimum magnitude of completeness ($M_c$), a key indicator of the completeness of an earthquake catalog, reflects the monitoring capability of a seismic network and serves as a crucial foundation for the accurate calculation of the b-value. We began by integrating multi-source earthquake catalogs for mainland China using the nearest-neighbor method. Building on this, we employed a combination of partitioned time-series analysis and a grid-based spatial scanning technique to systematically investigate the spatiotemporal evolution of the $M_c$ and the b-value across mainland China and its adjacent regions. Our findings indicate the following: (1) Since the 1980s, the overall trend of $M_c$ has shifted from high and unstable values to low and stable ones. However, significant earthquake events can cause a notable short-term increase in the $M_c$. (2) The b-value exhibits strong fluctuations, primarily influenced by the dual effects of the tectonic stress field and catalog completeness. These fluctuations are particularly pronounced in highly active seismic regions such as the Sichuan–Yunnan area and Taiwan, whereas the western Tibetan Plateau has consistently maintained a low b-value. (3) The spatial distributions of both the $M_c$ and the b-value are markedly heterogeneous. By developing a unified and complete earthquake catalog for mainland China, our research highlights the qualitative leap in monitoring capabilities brought about by the continuous densification and technological upgrading of seismic networks. This dataset provides a solid foundation for future seismological research, disaster prevention practices, and especially for the development of AI-based earthquake prediction models. Full article

Accurate ground deformation monitoring with interferometric synthetic aperture radar (InSAR) is often hindered by tropospheric delays caused by atmospheric pressure, temperature, and water vapor variations. While models such as ERA5 (European Centre for Medium-Range Weather Forecasts Reanalysis v5) provide first-order corrections, they often leave residual errors dominated by small-scale turbulent effects. To address this, we present a novel variational autoencoder with clustering (VAE-clustering) approach that performs unsupervised separation of atmospheric and deformation signals, followed by noise component removal via density-based clustering. The method is integrated into the MintPy pipeline for automated velocity and displacement time-series retrieval. We evaluate our approach on Sentinel-1 interferograms from three case studies: (1) land subsidence in Mashhad, Iran (2015–2022), (2) land subsidence in Tehran, Iran (2018–2021), and (3) postseismic deformation after the 2021 Acapulco earthquake. Across all cases, the method reduced the velocity standard deviation by approximately 70% compared to the ERA5 corrections, leading to more reliable displacement estimates. These results demonstrate that VAE-clustering can effectively mitigate residual tropospheric noise, improving the accuracy of large-scale InSAR time-series analyses for geohazard monitoring and related applications. Full article

Earthquake prediction remains a significant challenge in seismology, and advancements in signal processing techniques have opened new avenues for improving prediction accuracy. This paper explores the application of Time–Frequency Distributions (TFDs) to seismic signals to identify diagnostic precursory patterns of major earthquakes. TFDs provide a comprehensive analysis of the non-stationary nature of seismic data, allowing for the identification of precursory patterns based on energy concentration features. Current earthquake prediction models primarily focus on long-term forecasts, predicting events by identifying a cycle in historical data, or on nowcasting, providing alerts seconds after a quake has begun. However, both approaches offer limited utility for disaster management, compared to short-term earthquake prediction methods. This paper proposes a new possible precursory pattern of major earthquakes, tested through analysis of recent major earthquakes and their respective prior minor earthquakes for five earthquake-prone countries, namely Türkiye, Indonesia, the Philippines, New Zealand, and Japan. Precursors in the time–frequency domain have been consistently identified in all datasets within several hours or a few days before the major earthquakes occurred, which were not present in the observation and analysis of the earthquake catalogs in the time domain. This research contributes towards the ongoing efforts in earthquake prediction, highlighting the potential of quadratic non-linear TFDs as a significant tool for non-stationary seismic signal analysis. To the best of the authors’ knowledge, no similar approach for consistently identifying earthquake diagnostics precursors has been proposed, and, therefore, we propose a novel approach in reliable earthquake prediction using TFD analysis. Full article

Earthquakes are sudden and highly destructive events that severely disrupt infrastructure and logistics systems, making accurate and timely emergency material demand forecasting a critical challenge in disaster response. However, the scarcity of reliable data during the early stages of an earthquake limits the effectiveness of traditional forecasting methods. To address this issue, this study proposes a hybrid forecasting framework that integrates the Grey Model (GM(1,1)) with Bayesian Dynamic Linear Models (BDLMs), aiming to improve both the accuracy and adaptability of demand predictions. The approach operates in two phases: first, GM(1,1) generates preliminary forecasts using limited initial observations; second, BDLMs dynamically update these forecasts in real time as new data become available. The model is validated through a case study of the 2010 M7.1 Yushu earthquake in Qinghai Province, China. The results indicate that the hybrid method produces reliable forecasts even at the earliest stages of the disaster, with increasing accuracy as more observational data are incorporated. Our case study demonstrates that the integrated GM(1,1)-BDLM framework substantially reduces prediction errors compared to standalone GM(1,1). Using the first five days’ data to forecast fatalities and emergency material demand for days 6–10, the hybrid model achieves a 4.01% error rate—a 19.62 percentage point improvement over GM(1,1)’s 23.63% error rate. This adaptive forecasting mechanism offers robust support for evidence-based decision-making in emergency material allocation, enhancing the efficiency and responsiveness of post-disaster relief operations. Full article

Accurately forecasting emergency material demand during the initial stages of disaster response is challenging due to communication disruptions and data scarcity. This study proposes a hybrid model integrating regression analysis and intelligent analysis to estimate casualties and predict emergency supply needs indirectly. A case study of five earthquake-affected villages validates the model, using building collapse rates and population data to calculate casualties and determine the demand for essential supplies, including food, water, medicine, and tents. The findings demonstrate that the proposed approach effectively addresses the “black box” condition by utilizing correction factors for population density, disaster preparedness, and emergency response capacity, providing a structured framework for rapid and accurate demand forecasting in disaster scenarios. Full article

An event is an occurrence that takes place at a specific time and location that can be either weather-related (snowfall), social (crime), natural (earthquake), political (political unrest), or medical (pandemic) in nature. These events do not belong to the “normal” or “usual” spectrum and result in a change in a given situation; thus, their prediction would be very beneficial, both in terms of timely response to them and for their prevention, for example, the prevention of traffic accidents. However, this is currently challenging for researchers, who are called upon to manage and analyze a huge volume of data in order to design applications for predicting events using artificial intelligence and high computing power. Although significant progress has been made in this area, the heterogeneity in the input data that a forecasting application needs to process—in terms of their nature (spatial, temporal, and semantic)—and the corresponding complex dependencies between them constitute the greatest challenge for researchers. For this reason, the initial forecasting applications process data for specific situations, in terms of number and characteristics, while, at the same time, having the possibility to respond to different situations, e.g., an application that predicts a pandemic can also predict a central phenomenon, simply by using different data types. In this work, we present the forecasting applications that have been designed to date. We also present a model for predicting traffic accidents using categorical logic, creating a Knowledge Base using the Resolution algorithm as a proof of concept. We study and analyze all possible scenarios that arise under different conditions. Finally, we implement the traffic accident prediction model using the Prolog language with the corresponding Queries in JPL. Full article

This research explores the improvement of tsunami occurrence forecasting with machine learning predictive models using earthquake-related data analytics. The primary goal is to develop a predictive framework that integrates a wide range of data sources, including seismic, geospatial, and ecological data, toward improving the accuracy and lead times of tsunami occurrence predictions. The study employs machine learning methods, including Random Forest and Logistic Regression, for binary classification of tsunami events. Data collection is performed using a Kaggle dataset spanning 1995–2023, with preprocessing and exploratory analysis to identify critical patterns. The Random Forest model achieved superior performance with an accuracy of 0.90 and precision of 0.88 compared to Logistic Regression (accuracy: 0.89, precision: 0.87). These results underscore Random Forest’s effectiveness in handling imbalanced data. Challenges such as improving data quality and model interpretability are discussed, with recommendations for future improvements in real-time warning systems. Full article

Earthquake activity poses significant risks to both human survival and economic development. However, earthquake forecasting remains a challenge due to the complex, poorly understood interactions that drive seismic events. In this study, we construct an earthquake percolation model to examine the relationships between earthquakes and the underlying patterns and processes in Southern California. Our results demonstrate that the model can capture the spatiotemporal and magnitude characteristics of seismic activity. Through clustering analysis, we identify two distinct regimes: a continuous increase driven by earthquake clustering, and a discontinuous increase resulting from the merging of clusters dominated by large, distinct mega-earthquakes. Notably, in the continuous increase regime, we observe that clusters exhibit a broader spatiotemporal distribution, suggesting long-range and long-term correlations. Additionally, by varying the magnitude threshold, we explore the scaling behavior of earthquake percolation. The robustness of our findings is confirmed through comparison with multiple shuffling tests. Full article