Search Results (34)

In January 2025, a magnitude 6.8 earthquake struck Dingri County, Shigatse, Tibet, causing severe damage. Rapid and precise extraction of damaged buildings is essential for emergency relief and rebuilding efforts. This study proposes an approach integrating YOLO-E (Real-Time Seeing Anything) and the Segment Anything Model 2 (SAM2) to extract damaged buildings with multi-source remote sensing images, including post-earthquake Gaofen-7 imagery (0.80 m), Beijing-3 imagery (0.30 m), and pre-earthquake Google satellite imagery (0.15 m), over the affected region. In this hybrid approach, YOLO-E functions as the preliminary segmentation module for initial segmentation. It leverages its real-time detection and segmentation capability to locate potential damaged building regions and generate coarse segmentation masks rapidly. Subsequently, SAM2 follows as a refinement step, incorporating shapefile information from pre-disaster sources to apply precise, pixel-level segmentation. The dataset used for training contained labeled examples of damaged buildings, and the model optimization was carried out using stochastic gradient descent (SGD), with cross-entropy and mean squared error as the selected loss functions. Upon evaluation, the model reached a precision of 0.840, a recall of 0.855, an F1-score of 0.847, and an IoU of 0.735. It successfully extracted 492 suspected damaged building patches within a radius of 20 km from the earthquake epicenter, clearly showing the distribution characteristics of damaged buildings concentrated in the earthquake fault zone. In summary, this hybrid YOLO-E and SAM2 approach, leveraging multi-source remote sensing imagery, delivers precise and rapid extraction of damaged buildings with a precision of 0.840, recall of 0.855, and IoU of 0.735, effectively supporting targeted earthquake rescue and post-disaster reconstruction efforts in the Dingri County fault zone. Full article

Background and Objectives: On 6 February 2023, two catastrophic earthquakes struck southeastern Türkiye, affecting over 13 million individuals and causing widespread destruction. While the physical damage was immediate, the psychological consequences—particularly posttraumatic stress disorder (PTSD) and depression—have proven long-lasting. This study aimed to evaluate the severity and course of PTSD symptoms among survivors and to examine the effectiveness of a telepsychiatry-based mental health intervention in a post-disaster setting. Materials and Methods: This naturalistic, observational study included 153 adult participants from the affected regions who underwent at least two telepsychiatry sessions between the first and sixth month post-disaster. Initial screening was conducted using the General Health Questionnaire (GHQ-12), and individuals scoring ≥ 13 were further assessed with the PTSD Checklist—Civilian Version (PCL-C) and the Beck Depression Inventory (BDI). Follow-up evaluations and pharmacological or psychoeducational interventions were offered as clinically indicated. Results: At the one-month follow-up, 94.4% of participants met the threshold for PTSD symptoms (PCL-C > 22) and 77.6% had severe depressive symptoms (BDI > 30). By the sixth month, PTSD symptoms had significantly decreased (mean PCL-C score reduced from 42.47 ± 12.22 to 33.02 ± 12.23, p < 0.001). Greater symptom reduction was associated with higher educational attainment and perceived social support, while prior trauma predicted poorer outcomes. Depression severity emerged as the strongest predictor of chronic PTSD. Conclusions: This study highlights the psychological burden following the 2023 earthquakes in Türkiye and demonstrates the feasibility and potential effectiveness of telepsychiatry in disaster mental health care. Integrating digital mental health services into disaster response systems may help reach vulnerable populations and improve long-term psychological recovery. Full article

In subtropical regions, typhoons and tropical storms can generate massive rainstorms resulting in thousands of landslides, often termed as Multiple-Occurrence of Regional Landslide Events (MORLE). Understanding the hazards, their location, and their triggering mechanism can help to mitigate exposure and potential impacts. Extreme rainfall events and earthquakes frequently trigger destructive landslides that cause extensive economic loss, numerous fatalities, and significant damage to natural resources. However, inventories of rainfall-induced landslides suggest that they occur frequently under climate change. This study proposed a semi-automated time series algorithm that integrates Sentinel-2 and Integrated Multi-satellite Retrievals for Global Precipitation Measurements (GPM-IMERG) data to detect rainfall-induced landslides. Pixel-wise NDVI time series data are analyzed to detect change points, which are typically associated with vegetation loss due to landslides. These NDVI abrupt changes are further correlated with the extreme rainfall events in the GPM-IMERG dataset, within a defined time window, to detect RIL. The algorithm is tested and evaluated eight previously published landslide inventories, including both those manually mapped and those derived from high-resolution satellite data. The landslide detection yielded an overall F1-score of 0.82 and a mean producer accuracy of 87%, demonstrating a substantial improvement when utilizing moderate-resolution satellite data. This study highlights the combination of using optical images and rainfall time series data to detect landslides in remote areas that are often inaccessible to field monitoring. Full article

Construction projects in deep underground engineering are associated with the recording of massive amounts of diversified signals during real time and continuous microseismic monitoring given the complexity and specificity of the construction environment. Before the analysis of source information and further prediction of possible disasters, it is generally necessary to perform onset time picking and detection of microseismic signals. To improve the accuracy and efficiency of these tasks, this paper proposes an advanced deep dual-task neural network, which sequentially integrates the two processes. In this method, a score map is used to label the onset time of micro-fracture waveforms to improve the picking accuracy. The proposed model can simultaneously handle the onset time picking and detection tasks of microseismic signals to achieve optimal performance. Based on the similarity of data structures, the output from the onset time picking section is imported into the detection section to classify different types of microseismic waveforms. The onset time picking and detection procedures can be seamlessly integrated, where the score map of the onset time can help improve the detection accuracy. The results show that this method has a good performance for the onset time picking and detection of microseismic waveforms that are polluted by noises of various types and intensities. A comparison of the proposed method with existing methods and applications in underground engineering projects helped demonstrate the excellent performance of this method. The proposed approach can accelerate the automatic processing of microseismic signals and has significant potential for the exploration of seismology and earthquake research. Full article

This paper presents the Open-Source Intelligence Disaster Event Tracker (ODET), a modular platform that provides customizable endpoints and agents for each processing step. ODET enables the implementation of AI-enhanced algorithms to respond to various complex disaster scenarios. To evaluate ODET, we conducted two case studies using unmodified AI models to demonstrate its base performance and potential applications. Through our case studies on Hurricane Harvey and the 2023 Turkey earthquake, we show how complex tasks can be quickly broken down with ODET while achieving a score of up to 89% using the AlignScore metric. ODET enables compliance with Berkeley protocol requirements by ensuring data privacy and using privacy-preserving processing methods. Our results demonstrate that ODET is a robust platform for the long-term monitoring and analysis of dynamic environments and can improve the efficiency and accuracy of situational awareness reports in disaster management. Full article

Earthquake prediction is the holy grail of seismology and one of humanity’s greatest dreams. The Earth’s magnetic field appears to be one of the best possible precursors of earthquakes, although the topic is controversial. Recent advancements have made it possible to observe magnetic fields from satellites with great accuracy. We utilize magnetic measurements from Swarm satellites to explore the potential identification of anomalous magnetic signals preceding earthquakes. Focusing on 1077 major earthquakes that occurred in 2014–2023 in the Alpine–Himalayan belt, we apply an automatic algorithm to data recorded 10 days before each earthquake. This analysis reveals clear pre-earthquake anomalies in the magnetic field components. Notably, a robust correlation is established between the duration of these anomalies and the earthquake magnitude, indicating that as the earthquake magnitude increases, so does the duration of the anomaly. Here we show that this method has a great ability to make predictions (high accuracy 79%, precision 88%, F1-score and hit rate 84%), thus becoming the basis for an Operational Earthquake Prediction System (OEPS). Full article

The great 2015 Nepal earthquake of magnitude 7.6 killed about 9000 people. To better ensure a more coordinated disaster response, a Basic On-Site Disaster Medical Support (BOS-DMS) course was designed in 2017. This study evaluates the effectiveness of the BOS-DM course. The course was conducted twice and attended by 135 participants, of whom 113 (83.7%) answered pre-test and post-test based multiple-choice questions. Qualitative and quantitative feedback was provided by 94 participants (69.6%). Mean test scores for the participants increased from 4.24 ± 1.42 to 6.55 ± 2.16 (p-value < 0.0001; paired t-test). More than 92.0% of participants felt that the course prepared healthcare workers to manage acute medical situations at a disaster site. Subject knowledge scores increased from 34.8% to 90.2%. A three-day BOS_DMS course has the potential to improve on-site disaster management knowledge. Our study noted that precise scheduling, making attendance compulsory, translating course materials into the local language, inclusion of disaster exercises and training local master trainers can enhance course effectiveness. Full article

Geomagnetic field data have been found to contain earthquake (EQ) precursory signals; however, analyzing this high-resolution, imbalanced data presents challenges when implementing machine learning (ML). This study explored feasibility of principal component analyses (PCA) for reducing the dimensionality of global geomagnetic field data to improve the accuracy of EQ predictive models. Multi-class ML models capable of predicting EQ intensity in terms of the Mercalli Intensity Scale were developed. Ensemble and Support Vector Machine (SVM) models, known for their robustness and capabilities in handling complex relationships, were trained, while a Synthetic Minority Oversampling Technique (SMOTE) was employed to address the imbalanced EQ data. Both models were trained on PCA-extracted features from the balanced dataset, resulting in reasonable model performance. The ensemble model outperformed the SVM model in various aspects, including accuracy (77.50% vs. 75.88%), specificity (96.79% vs. 96.55%), F1-score (77.05% vs. 76.16%), and Matthew Correlation Coefficient (73.88% vs. 73.11%). These findings suggest the potential of a PCA-based ML model for more reliable EQ prediction. Full article

This study focuses on the identification of collapsed buildings in satellite images after earthquakes through deep learning-based image segmentation models. The performance of four different architectures, namely U-Net, LinkNet, FPN, and PSPNet, was evaluated using various performance metrics, such as accuracy, precision, recall, F1 score, specificity, AUC, and IoU. The study used satellite images taken from the area located in the south and southeast of Türkiye covering the eleven provinces which are most affected by the Mw 7.7 Pazarcık (Kahramanmaraş) and Mw 7.6 Elbistan (Kahramanmaraş) earthquakes. The results indicated that FPN and U-Net were the best-performing models depending on the performance metric of interest. FPN achieved the highest accuracy and specificity scores, as well as the best precision score, while U-Net achieved the best recall and F1 score values, as well as the best AUC and IoU scores. The training and validation accuracy and loss curves were analyzed, and the results indicated that all four models achieved an accuracy value of over 96%. The FPN model outperformed the others in terms of accurately segmenting images while maintaining a low loss value. This study provides insights into the potential of deep learning-based image segmentation models in disaster management and can be useful for future research in this field. Full article

Here, we employ natural time analysis of seismicity together with non-extensive statistical mechanics aiming at shortening the occurrence time window of the Kahramanmaraş-Gazientep M7.8 earthquake. The results obtained are in the positive direction pointing to the fact that after 3 February 2023 at 11:05:58 UTC, a strong earthquake was imminent. Natural time analysis also reveals a minimum fluctuation of the order parameter of seismicity almost three and a half months before the M7.8 earthquake, pointing to the initiation of seismic electrical activity. Moreover, before this earthquake occurrence, the detrended fluctuation analysis of the earthquake magnitude time-series reveals random behavior. Finally, when applying earthquake nowcasting, we find average earthquake potential score values which are compatible with those previously observed before strong ($M\ge 7.1$) earthquakes. The results obtained may improve our understanding of the physics of crustal phenomena that lead to strong earthquakes. Full article

Green’s function plays an important role in the relationship of a future strong earthquake epicenter to the average earthquake potential score. In the frame of the latter, the fractal dimension of the unified scaling law for earthquakes naturally arises. Here it is also shown to be a cornerstone for the development of a new ambient noise tomography methodology, which is applied for example to the west coast of Central Greece. In particular, we show that a fast and reliable 3D shear velocity model extraction is possible without the need for a large amount of data, great-circle propagation assumptions, or the intermediate step of inverting for group velocity maps. The tomography results are consistent with previous studies conducted in the neighboring region. Full article

The process of manually checking, validating, and excluding data in an ionospheric very-low-frequency (VLF) analysis during extreme events is a labor-intensive and time-consuming task. However, this task can be automated through the utilization of machine learning (ML) classification techniques. This research paper employed the Random Forest (RF) classification algorithm to automatically classify the impact of solar flares on ionospheric VLF data and erroneous data points, such as instrumentation errors and noisy data. The data used for analysis were collected during September and October 2011, encompassing solar flare classes ranging from C2.5 to X2.1. The F1-score values obtained from the test dataset displayed values of 0.848; meanwhile, a more detailed analysis revealed that, due to the imbalanced distribution of the target class, the per-class F1-score indicated higher values for the normal data point class (0.69–0.97) compared to those of the anomalous data point class (0.31 to 0.71). Instances of successful and inadequate categorization were analyzed and presented visually. This research investigated the potential application of ML techniques in the automated identification and classification of erroneous VLF amplitude data points; however, the findings of this research hold promise for the detection of short-term ionospheric responses to, e.g., gamma ray bursts (GRBs), or in the analysis of pre-earthquake ionospheric anomalies. Full article

On 6 February, southeastern Turkey and parts of Syria were struck by two powerful earthquakes, one measuring a magnitude of 7.8 and the other, nine hours later, at a magnitude of 7.5. These earthquakes have been recorded as some of the deadliest natural disasters worldwide since the 2010 Haiti earthquake, impacting around 14 million people in Turkey. For trauma survivors, the stressors associated with an event can lead to the development of acute stress disorder (ASD) or other psychiatric disorders. Trauma experiences during adolescence can impact development and affect adolescents differently than adults. Although ASD in adults has been addressed in several studies, there is much less information available about how younger populations respond to acute stress. The aim of our study was to assess the occurrence of ASD among individuals seeking help at the Şanlıurfa Mehmet Akif İnan Research and Training Hospital Child and Adolescent Outpatient Clinic following the 2023 Turkey Earthquakes and the factors associated with acute stress reactions. A child and adolescent psychiatry specialist conducted psychiatric interviews with the adolescents, and the individuals were also asked to complete ‘The National Stressful Events Survey Acute Stress Disorder Short Scale’ (NSESSS) to evaluate acute stress symptoms. ASD diagnoses were established according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) criteria. Results showed that 81.6% of the participants (n = 49) were diagnosed with ASD, and drug treatment was initiated in 61.7% of the cases (n = 37). It was determined that ASD rates did not differ according to gender, and patients without physical injury had higher acute stress symptom scores (p > 0.05). According to the logistic regression models, paternal educational levels and adolescents’ own requests for psychiatric assistance were predictors of acute stress disorder (OR 10.1, β = 2.31, p = 0.006 and OR 16.9, 95 β = 2.83, p = 0.001, respectively). Our findings revealed striking results in demonstrating the need for careful evaluation of adolescents without physical injury in terms of acute stress disorder and the need to pay close attention to the psychiatric complaints of adolescents willing to seek mental health assistance. Moreover, our study suggests that the proportion of adolescents experiencing acute stress symptoms after earthquakes might be higher than previously reported. Estimation of the incidence rate and symptoms of psychiatric distress in the short-term period following a disaster is important for establishing disaster epidemiology and implementing efficient relief efforts in the early stages. The outcomes of this study have the potential to yield novel insights into the realms of disaster mental health and emergency response policies, as well as their pragmatic implementations. Full article

Structures in their service life are often damaged as a result of aging or extreme events such as earthquakes or storms. It is essential to detect damage in a timely fashion to ensure the safe operation of the structure. If left unchecked, subsurface damage (SSD) can cause significant internal damage and may result in premature structural failure. In this study, a Convolutional Neural Network (CNN) has been developed for SSD detection using surface strain measurements. The adopted network architecture is capable of pixel-level image segmentation, that is, it classifies each location of strain measurement as damaged or undamaged. The CNN which is fed full-field strain measurements as an input image of size 256 × 256 projects the SSD onto an output image of the same size. The data for network training is generated by numerical simulation of aluminum bars with different damage scenarios, including single damage and double damage cases at a random location, direction, length, and thickness. The trained network achieves an Intersection over Union (IoU) score of 0.790 for the validation set and 0.794 for the testing set. To check the applicability of the trained network on materials other than aluminum, testing is performed on a numerically generated steel dataset. The IoU score is 0.793, the same as the aluminum dataset, affirming the network’s capability to apply to materials exhibiting a similar stress–strain relationship. To check the generalization potential of the network, it is tested on triple damage cases; the IoU score is found to be 0.764, suggesting that the network works well for unseen damage patterns as well. The network was also found to provide accurate predictions for real experimental data obtained from Strain Sensing Smart Skin (S⁴). This proves the efficacy of the network to work in real-life scenarios utilizing the full potential of the novel full-field strain sensing methods such as S⁴. The performance of the proposed network affirms that it can be used as a non-destructive testing method for subsurface crack detection and localization. Full article

This study aims to highlight the degree of perception of the young population (18–35 years old), from the northern region of Oltenia (Gorj County, Romania), regarding the impact of the wave of seismic strong ground motions recorded in the region, starting from 13 February 2023, on some indicators to characterize the sustainable development of the region, in particular, the entrepreneurial potential of the region and the quality of life of the affected population. It was considered opportune to carry out this study, considering the novelty of such a situation, as the respective geographical area has not been subjected to strong ground motions in the recent past. This study was built on the basis of the questionnaire applied to 599 people, with permanent residence in Gorj County and aged between 18 and 35 years. The data were collected between 27 February 2023 and 31 March 2023, more than fourteen days after the first recorded micro-seismic event. The main working method is the combined statistical analysis, on the one hand, of the notification and evaluation of the respondents’ degree of information regarding the manifestation of the wave of seismic strong ground motions, the perception of the authorities’ involvement in the management of its negative effects (material damage and effects on the regional entrepreneurial potential), the negative impact on some indicators for evaluating the sustainable development of the region, and, on the other hand, the engagement in the empirical research of the phenomenon, related to the objectives of sustainable development, in accordance with the bibliography available. This study reveals that, in the context of a very high degree of information of the respondents regarding the manifestation of the wave of seismic strong ground motions (about 95%) and the reasonable degree of access to resources and credible information materials (55.2%), only 45.4% of them expressed their high confidence in the action of the authorities to limit the negative effects of seismic strong ground motions. On the other hand, this study highlights that the highest satisfaction average of the population, among the four WHOQOL-BREF domains, is represented by the “Psychological” domain (75.33 ± 21.17), and the lowest average is represented by the “Environmental” domain (67.45 ± 20.90). This study also reveals that male respondents show a higher satisfaction average than that recorded in the case of female respondents in the “Physical”, Psychological”, and “Environmental” domains; for the “Social” domain, the differences are insignificant in favor of the respondents from the second category. The respondents domiciled in the rural environment compared to those domiciled in the urban environment register higher mean scores in all four domains of the quality of life analysis; the respondents with higher education have a higher average score in the “Physical”, “Psychological”, and “Social” domains, with the exception of the “Environmental” domain. The quality of life indicators for the people in the area affected by earthquakes are adversely influenced by their concerns regarding the potential harm to the region’s touristic and entrepreneurial potential. Specifically, those with a high level of belief in the potential harm to the tourism and entrepreneurial potential of the region have lower quality of life measures than those with a low level of belief. Additionally, individuals with a high level of trust in the authorities’ measures to limit the negative impacts of the earthquakes have better quality of life measures than those with low trust. Full article