Search Results (13)

The integration of deep learning and remote sensing for the rapid detection of landslides from high-resolution remote sensing imagery plays a crucial role in post-disaster emergency response. However, the availability of publicly accessible deep learning datasets specifically for landslide detection remains limited, posing challenges for researchers in meeting task requirements. To address this issue, this study develops and releases a deep learning landslide dataset using Google Earth imagery, focusing on the impact zones of the 2008 Wenchuan Ms8.0 earthquake, the 2014 Ludian Ms6.5 earthquake, and the 2017 Jiuzhaigou Ms7.0 earthquake as the research areas. The dataset contains 2727 samples with a spatial resolution of 1.06 m. To enhance landslide recognition, a lightweight boundary-focused attention (BFA) mechanism designed using the Canny operator is adopted. This mechanism improves the model’s ability to emphasize landslide edge features and is integrated with the ResUNet model, forming the ResUNet–BFA architecture for landslide identification. The experimental results indicate that the ResUNet–BFA model outperforms widely used algorithms in extracting landslide boundaries and details, resulting in fewer misclassifications and omissions. Additionally, compared with conventional attention mechanisms, the BFA achieves superior performance, producing recognition results that more closely align with actual labels. Full article

Influenced by the combined effects of crustal uplift and river downcutting, rivers with significant potential energy are often found in high mountain and canyon areas. Due to the active tectonic movements that these areas have experienced or are currently experiencing, geological hazards frequently occur on the mountains flanking the rivers. Therefore, evaluating the susceptibility and risk of earthquake landslides in river segments of these high mountain and canyon areas is of great importance for disaster prevention and mitigation, as well as for the safe construction and operation of hydropower stations. Currently, a major challenge in the study of landslide susceptibility and hazard is determining the thickness of potential landslide bodies. The presence of excess topography reflects the instability of the disrupted slopes, which is also a fundamental cause of landslides. This study takes the example of the Ludian earthquake in 2014, focusing on the IX and VIII intensity zones, to extract the excess topography in the study area and analyze its correlation with seismic landslides. The correlation between the critical acceleration value and the excess topography was validated using the Spearman’s rank correlation coefficient, resulting in a correlation coefficient of −0.771. This indicates a strong negative correlation between the excess topography and critical acceleration, with significant relevance. The landslide susceptibility distribution obtained by setting the potential landslide thickness based on the excess topography and proportion coefficient showed an ROC curve analysis AUC value of 0.829. This is higher than the AUC value of 0.755 for the landslide susceptibility result using a uniform potential landslide thickness of 3 m, indicating the higher model evaluation accuracy of this approach. Earthquake landslide hazard predictions for rapid post-earthquake assessments and earthquake landslide hazard zoning for pre-earthquake planning were made using actual seismic ground motion and a 2% exceedance probability in 50 years, respectively. Comparing these with the 10,559 coseismic landslides triggered by the Ludian earthquake and evaluating the seismic landslide development rate, the results were found to be consistent with reality. The improved model better reflects the control of excess topography and rock mechanics properties on the development of earthquake landslide hazards on high steep slopes. Identifying high-risk seismic landslide areas through this method and taking corresponding preventive and protective measures can help plan and construct safer hydropower and other infrastructure, thereby enhancing their disaster resistance. Full article

Earthquake-triggered landslides (ETLs) feature large quantities, extensive distributions, and enormous losses to human lives and critical infrastructures. Near-real spatial prediction of ETLs can rapidly predict the locations of coseismic landslides just after a violent earthquake and is a vital technical support for emergency response. However, near-real prediction of ETLs has always been a great challenge with relatively low accuracy. This work proposes an ensemble prediction model of EnPr by integrating machine learning tree models and a deep learning convolutional neural network. EnPr exhibits relatively strong prediction and generalization performance and achieves relatively accurate prediction of ETLs. Six great seismic events occurring from 2008 to 2022 on the southeastern margin of the Tibetan Plateau are selected to conduct ETL prediction. In a chronological order, the 2008 M_s 8.0 Wenchuan, 2010 M_s 7.1 Yushu, 2013 M_s 7.0 Lushan, and 2014 M_s 6.5 Ludian earthquakes are employed for model training and learning. The 2017 M_s 7.0 Jiuzhaigou and 2022 M_s 6.1 Lushan earthquakes are adopted for ETL prediction. The prediction accuracy merits of ACC and AUC attain 91.28% and 0.85, respectively, for the Jiuzhaigou earthquake. The values of ACC and AUC achieve 93.78% and 0.88, respectively, for the Lushan earthquake. The proposed EnPr algorithm outperforms the algorithms of XGBoost, random forest (RF), extremely randomized trees (ET), convolutional neural network (CNN), and Transformer. Moreover, this work reveals that seismic intensity, high and steep relief, pre-seismic fault tectonics, and pre-earthquake road construction have played significant roles in coseismic landslide occurrence and distribution. The EnPr model uses globally accessible open datasets and can therefore be used worldwide for new large seismic events in the future. Full article

A high-resolution catalog for the 2014 Ms 6.5 Ludian aftershocks was constructed based on the deep learning phase-picking model (CERP) and seismic-phase association technology (PALM). A specific training strategy, which combines the advantages of the conventional short–long window average energy ratio algorithm (STA/LTA) and AI algorithms, is employed to retrain the CERP model. The P- and S-wave phases were accurately detected and picked on continuous seismic waveforms by the retained AI model. Hypoinverse and HypoDD were utilized for the precise location of 3286 events. Compared to the previous results, our new catalog exhibits superior performances in terms of location accuracy and the number of aftershock events, thereby enabling a more detailed depiction of the deep-seated tectonic features. According to the distribution of aftershocks, it can be inferred that (1) the seismogenic fault of the Ludian earthquake is the NW-trending Baogunao–Xiaohe Fault, (2) the Ludian aftershocks interconnected with the discontinuous NW-trending Baogunao–Xiaohe Fault, and they also intersected with the Zhaotong–Ludian Fault. (3) This suggests that the NE-trending Zhaotong–Ludian Fault may have been intersected by the NW-trending Baogunao–Xiaohe Fault, indicating that the Baogunao–Xiaohe Fault is likely a relatively young Neogene fault. Full article

Coseismic landslides pose a significant threat to the sustainability of both the natural environment and the socioeconomic fabric of society. This escalation in earthquake frequency has driven a growing interest in regional-scale assessment techniques for these landslides. The widely adopted infinite slope model, introduced by Newmark, is commonly utilized to assess coseismic landslide hazards. However, this conventional model falls short of capturing the influence of rock mass structure on slope stability. A novel methodology was previously introduced, considering the roughness of potential slide surfaces on the inner slope, offering a fresh perspective on coseismic landslide hazard mapping. In this paper, the proposed method is recalibrated using new datasets from the 2013 Lushan earthquake. The datasets encompass geological units, peak ground acceleration (PGA), and a high-resolution digital elevation model (DEM), rasterized at a grid spacing of 30 m. They are integrated within an infinite slope model, employing Newmark’s permanent deformation analysis. This integration enables the estimation of coseismic displacement in each grid area resulting from the 2013 Lushan earthquake. To validate the model, the simulated displacements are compared with the inventory of landslides triggered by the Lushan earthquake, allowing the derivation of a confidence level function that correlates predicted displacement with the spatial variation of coseismic landslides. Ultimately, a hazard map of coseismic landslides is generated based on the values of the certainty factor. The analysis of the area under the curve is utilized to illustrate the improved effectiveness of the proposed method. Comparative studies with the 2014 Ludian earthquake reveal that the coseismic landslides triggered by the 2013 Lushan earthquake predominantly manifest as shallow rock falls and slides. Brittle coseismic fractures are often associated with reverse seismogenic faults, while complaint coseismic fractures are more prevalent in strike–slip seismogenic faults. The mapping procedure stands as a valuable tool for predicting seismic hazard zones, providing essential insights for decision-making in infrastructure development and post-earthquake construction endeavors. Full article

The 2014 Mw 6.2 Ludian earthquake exhibited a structurally complex source rupture process and an unusual spatial distribution pattern of co-seismic landslides. In this study, we constructed a spatial database consisting of 1470 co-seismic landslides, each exceeding 500 m². These landslides covered a total area of 8.43 km² and were identified through a comprehensive interpretation of high-resolution satellite images taken before and after the earthquake. It is noteworthy that the co-seismic landslides do not exhibit a linear concentration along the seismogenic fault; instead, they predominantly extend along major river systems with an NE–SW trend. Moreover, the southwest-facing slopes have the highest landslide area ratio of 1.41. To evaluate the susceptibility of the Ludian earthquake-triggered landslides, we performed a random forest model that considered topographic factors (elevation, slope, aspect, distance to rivers), geological factors (lithology), and seismic factors (ground motion parameters, epicentral distance, distance to the seismogenic fault). Our analysis revealed that the distance to rivers and elevation were the primary factors influencing the spatial distribution of the Ludian earthquake-triggered landslides. When we considered the directional variation in ground motion parameters, the AUC of the model slightly decreased. However, incorporating this variation led to a significant reduction in the proportion of areas classified as “high” and “very high” landslide susceptibility. Moreover, SED^d emerged as the most effective ground motion parameter for interpreting the distribution of the co-seismic landslides when compared to PGA^d, PGV^d, and Ia^d. Full article

Social media data are constantly updated, numerous, and characteristically prominent. To quickly extract the needed information from the data to address earthquake emergencies, a topic-words detection model of earthquake emergency microblog messages is studied. First, a case analysis method is used to analyze microblog information after earthquake events. An earthquake emergency information classification hierarchy is constructed based on public demand. Then, subject sets of different granularities of earthquake emergency information classification are generated through the classification hierarchy. A detection model of new topic-words is studied to improve and perfect the sets of topic-words. Furthermore, the validity, timeliness, and completeness of the topic-words detection model are verified using 2201 messages obtained after the 2014 Ludian earthquake. The results show that the information acquisition time of the model is short. The validity of the whole set is 96.96%, and the average and maximum validity of single words are 78% and 100%, respectively. In the Ludian and Jiuzhaigou earthquake cases, new topic-words added to different earthquakes only reach single digits in validity. Therefore, the experiments show that the proposed model can quickly obtain effective and pertinent information after an earthquake, and the complete performance of the earthquake emergency information classification hierarchy can meet the needs of other earthquake emergencies. Full article

On 3 August 2014, an Mw 6.2 earthquake occurred in Ludian, Yunnan Province, China (27.245° N 103.427° E). This damaging earthquake caused approximately 400 fatalities, 1800 injuries, and the destruction of at least 12,000 houses. Using air temperature data of the National Center for Environmental Prediction (NCEP) and the tidal force fluctuant analysis (TFFA) method, we derive the temperature variations in multiple air layers between before and after the Ludian earthquake. In the spatial range of 30° × 30° (12°–42° N, 88°–118° E) of China, a thermal anomaly appeared only on or near the epicenter before earthquake, and air was heated from the land, then uplifted by a heat flux, and then cooled and dissipated upon rising. With the approaching earthquake, the duration and range of the thermal anomaly during each tidal cycle was found to increase, and the amplitude of the thermal anomaly varied with the tidal force potential: air temperature was found to rise during the negative phase of the tidal force potential, to reach peak at its trough, and to attenuate when the tidal force potential was rising again. A significance test supports the hypothesis that the thermal anomalies are physically related to Ludian earthquakes rather than being coincidences. Based on these results, we argue that the change of air temperature could reflect the stress changes modulated under the tidal force. Moreover, unlike the thermal infrared remote sensing data, the air temperature data provided by NCEP are not affected by clouds, so it has a clear advantage for monitoring the pre-earthquake temperature variation in cloudy areas. Full article

A time-frequency method retrieving the acceleration changes in the terminal stage of the
M6.1 Ludian earthquake in China is discussed in this article. The non-linear, non-stationary
seismo-demformation was obtained by using the Hilbert–Huang transform and followed by a
band-pass filter. We found that the temporal evolution of the residual GNSS-derived orientation
exhibits a unique disorder-alignment-disorder sequence days before the earthquake which
corresponds well with the four stages of an earthquake: elastic strain buildup, crack developments,
deformation, and the terminal stage of material failure. The disordering orientations are gradually
aligned with a common direction a few days before the terminal stage. This common direction is
consistent with the most compressive axis derived from the seismological method. In addition, the
region of the stress accumulation, as identified by the size of the disordered orientation, is
generally consistent with the earthquake preparation zones estimated by using numerical models. Full article

Although the Zhaotong–Ludian fault is a seismically active zone located in the boundary between the Sichuan–Yunnan block and the South China block, it has not experienced a large earthquake greater than Mw 7 since at least 1700. On 3 August, 2014, an Mw 6.1 earthquake (the Ludian earthquake) ruptured the Zhaotong active belt in Ludian County, Yunnan province, China. This earthquake was the largest earthquake recorded in the region since 2000, and it provides us with a unique opportunity to study the active tectonics in the region. The analysis of the aftershocks showed that two conjugate faults could have been involved in the event. We first used Global Positioning System (GPS) data and C-band RADARSAT-2 imagery to map the coseismic surface deformation. We then inverted the derived coseismic deformation for the slip distribution based on the constructed conjugate fault model. Finally, the coulomb failure stress due to the Ludian earthquake was estimated to investigate the potential seismic hazards in this region. Our investigations showed that the Ludian earthquake was mainly a bilateral rupture event. The major slip of the main shock was located at depths of 0–5 km, which is close but does not superpose with the aftershocks that are mostly located at depths of 5–20 km. Interestingly, the seismic moment released by the aftershocks (6.9 × 10¹⁸ N∙m) was greater than that of the main shock (2.6 × 10¹⁸ N∙m). This evidence suggests that the accumulated elastic strain at depths of 0–20 km could have been fully released by the Ludian earthquake and its subsequent aftershocks. Furthermore, our analysis of the coulomb failure stress changes due to the main shock showed that the aftershocks could be the result of dynamic triggering rather than static triggering. Full article

We propose a new convolutional neural networks method in combination with ordinal regression aiming at assessing the degree of building damage caused by earthquakes with aerial imagery. The ordinal regression model and a deep learning algorithm are incorporated to make full use of the information to improve the accuracy of the assessment. A new loss function was introduced in this paper to combine convolutional neural networks and ordinal regression. Assessing the level of damage to buildings can be considered as equivalent to predicting the ordered labels of buildings to be assessed. In the existing research, the problem has usually been simplified as a problem of pure classification to be further studied and discussed, which ignores the ordinal relationship between different levels of damage, resulting in a waste of information. Data accumulated throughout history are used to build network models for assessing the level of damage, and models for assessing levels of damage to buildings based on deep learning are described in detail, including model construction, implementation methods, and the selection of hyperparameters, and verification is conducted by experiments. When categorizing the damage to buildings into four types, we apply the method proposed in this paper to aerial images acquired from the 2014 Ludian earthquake and achieve an overall accuracy of 77.39%; when categorizing damage to buildings into two types, the overall accuracy of the model is 93.95%, exceeding such values in similar types of theories and methods. Full article

The 3 August 2014 Ludian earthquake with a moment magnitude scale (Mw) of 6.1 induced widespread landslides in the Ludian County and its vicinity. This paper presents a preliminary analysis of the distribution patterns and characteristics of these co-seismic landslides. In total, 1826 landslides with a total area of 19.12 km² triggered by the 3 August 2014 Ludian earthquake were visually interpreted using high-resolution aerial photos and Landsat-8 images. The sizes of the landslides were, in general, much smaller than those triggered by the 2008 Wenchuan earthquake. The main types of landslides were rock falls and shallow, disrupted landslides from steep slopes. These landslides were unevenly distributed within the study area and concentrated within an elliptical area with a 25-km NW–SE striking long axis and a 15-km NW–SE striking short axis. Three indexes including landslides number (LN), landslide area ratio (LAR), and landslide density (LD) were employed to analyze the relation between the landslide distribution and several factors, including lithology, elevation, slope, aspect, distance to epicenter and distance to the active fault. The results show that slopes consisting of deeply weathered and fractured sandstones and mudstones were the more susceptible to co-seismic landslides. The elevation range of high landslide susceptibility was between 900–1300 m and 1800–2000 m. There was a generally positive correlation between co-seismic landslides and slope angle, until a maximum for the slope class 40°–50°. The co-seismic landslides occurred preferably on Southeast (SE), South (S) and Southwest (SW) oriented slopes. Results also show that the landslide concentration tends to decrease with distance from the surface projection of the epicenter rather than the seismogenic fault, and the highest landslide concentration is located within a 5–6 km distance of the seismogenic fault. Regarding the epicenter, the largest landslide clusters were found on the SE, northeast by east (NEE) and nearly West (W) of the epicenter. In addition, we also suggest that statistical results of slope gradients of landslides might imply a threshold topography of the study area within a tectonically active background. By analogy with other events, the statistical results of landslides aspects also imply the seismogenic fault of the Ludian earthquake might have been the Northwest (NW)-trending fault, which is consistent with other studies. Full article

Background: An earthquake struck Ludian in Yunnan Province (China) on 3 August 2014, resulting in 3143 injuries, 617 deaths, and 112 missing persons. Our study aimed at estimating the health status and associated determinants among adolescent survivors after the Ludian earthquake. Methods: A cross-sectional survey of 845 was conducted at the Ludian No. 1 Middle School. Descriptive statistics, t-tests, ANOVA and stepwise linear regression analysis were used for data analysis. Results: The mean scores on the physical component summary (PCS) and mental component summary (MCS) were 46.23 (SD = 7.10) and 36.34 (SD = 7.09), respectively. Lower PCS scores in the aftermath of an earthquake were associated with being trapped or in danger, being female, being an ethnic minority, injury to self and house damage, while lower MSC scores were associated with fear during the earthquake, Han ethnicity, death in the family, not being involved in the rescue and low household income. Conclusions: In our study, significant associations between demographic, socio-economic, and trauma-related experiences variables and overall physical and mental health of adolescent survivors were presented. The results of this study help expand our knowledge of health status among adolescent survivors after the Ludian earthquake. Full article