Jiuzhaigou Earthquake Research Articles

The latent transition of posttraumatic stress disorder and growth among adolescents surviving Jiuzhaigou earthquake.

Knowledge of longitudinal changes in posttraumatic stress disorder (PTSD) and posttraumatic growth (PTG) may help survivors recover better. Yet, researchers dispute the population-based typologies of PTSD and PTG as well as the temporal transition between these subpopulations across time, especially among adolescents. Thus, the transition pattern still needs further research. Besides, parent-child factors (parenting styles, parent-child cohesion, and parental attachment styles) may influence the transition, but it keeps unclear. In the study, we aimed to solve these questions. A three-wave investigation was conducted among 620 adolescents, 12 months, 21 months, and 27 months, after the Jiuzhaigou earthquake by using self-report measures on PTSD, PTG, and parent-child factors at the three time points. Finally, 339 of them finished the three-wave investigation. Latent profile analysis models showed that three heterogeneous classes of PTSD and PTG existed across time: low-affected (low-level PTSD and PTG), thriving (low-level PTSD but high-level PTG), and struggling (high-level PTSD and PTG) groups. Random intercept latent transition analysis model suggested that samples mainly stayed in the original classes across time, with three main transitional paths: from struggling group to thriving group, from thriving group to low-affected group, and from low-affected group to struggling group. Besides, the study also found that parental rejection, overprotection, and anxious attachment were the possible factors that kept the stability of the struggling group. Parent-child cohesion increased the stability of the thriving group across time. Anxious attachment may worsen PTSD among adolescents and lower the stability of low-affected groups across time. Coexisting and transitional patterns exist in PTSD and PTG across time. Wrong parenting styles and insecure attachments can exacerbate PTSD symptoms and diminish adolescents' resilience, but parent-child cohesion can facilitate their growth after trauma. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Disentangling the Spatiotemporal Dynamics, Drivers, and Recovery of NPP in Co-Seismic Landslides: A Case Study of the 2017 Jiuzhaigou Earthquake, China

The 2017 Jiuzhaigou earthquake, registering a magnitude of 7.0, triggered a series of devastating geohazards, including landslides, collapses, and mudslides within the Jiuzhaigou World Natural Heritage Site. These destructive events obliterated extensive tracts of vegetation, severely compromising carbon storage in the terrestrial ecosystems. Net Primary Productivity (NPP) reflects the capacity of vegetation to absorb carbon dioxide. Accurately assessing changes in NPP is crucial for unveiling the recovery of terrestrial ecosystem carbon storage after the earthquake. To this end, we designed this study using the Moderate Resolution Imaging Spectroradiometer (MODIS) Net Primary Productivity datasets. The findings are as follows. NPP in the co-seismic landslide areas remained stable between 525 and 575 g C/m2 before the earthquake and decreased to 533 g C/m2 after the earthquake. This decline continued, reaching 483 g C/m2 due to extreme rainfall events in 2018, 2019, and 2020. Recovery commenced in 2021, and by 2022, NPP had rebounded to 544 g C/m2. The study of NPP recovery rate revealed that, five years after the earthquake, only 18.88% of the co-seismic landslide areas exhibited an NPP exceeding the pre-earthquake state. However, 17.14% of these areas had an NPP recovery rate of less than 10%, indicating that recovery has barely begun in most areas. The factor detector revealed that temperature, precipitation, and elevation significantly influenced NPP recovery. Meanwhile, the interaction detector highlighted that lithology, slope, and aspect also played crucial roles when interacting with other factors. Therefore, the recovery of NPP is not determined by a single factor, but rather by the interactions among various factors. The ecosystem resilience study demonstrated that the current recovery of NPP primarily stems from the restoration of grassland ecosystems. Overall, while the potential for NPP recovery in co-seismic landslide areas is optimistic, it will require a considerable amount of time to return to the pre-earthquake state.

Near-Real Prediction of Earthquake-Triggered Landslides on the Southeastern Margin of the Tibetan Plateau

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 Ms 8.0 Wenchuan, 2010 Ms 7.1 Yushu, 2013 Ms 7.0 Lushan, and 2014 Ms 6.5 Ludian earthquakes are employed for model training and learning. The 2017 Ms 7.0 Jiuzhaigou and 2022 Ms 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.

Utilizing a single-temporal full polarimetric Gaofen-3 SAR image to map coseismic landslide inventory following the 2017 Mw 7.0 Jiuzhaigou earthquake (China)

On August 8, 2017, a magnitude 7.0 earthquake struck Jiuzhaigou County in Sichuan Province, triggering numerous coseismic landslides. The prompt identification of these landslides is imperative for emergency rescue efforts and post-earthquake hazard assessments. Optical satellite and unmanned aerial vehicle (UAV) images are often obstructed by cloud cover and fog following earthquakes. In contrast, polarimetric synthetic aperture radar (PolSAR), unaffected by adverse weather conditions, emerges as an indispensable tool. However, the utilization of spaceborne single-temporal SAR for mapping the inventory of coseismic landslides is infrequent and encounters constraints due to several limitations. In this study, we analyzed the amplitude feature and polarimetric decomposition of multiple ground categories in a full PolSAR image, and proposed an automated method to accurately identify coseismic landslides using a single-temporal full PolSAR image. The coseismic landslide inventory following the Jiuzhaigou Earthquake was mapped and validated using high-resolution UAV images. Detailed analysis was conducted to identify error sources leading to omissions and false positives. Additionally, we evaluated various machine learning models to compare their performance with our proposed method. Finally, we conducted a comprehensive discussion on the strengths and weaknesses of different data types (PolSAR, optical satellite, and UAV) for coseismic landslide identification. Our results indicate that the proposed PolSAR-based method achieves high accuracy in coseismic landslide inventory mapping, offering an effective solution for timely post-earthquake emergency responses in complex environments and all weather conditions in the future.

An essential update on the inventory of landslides triggered by the Jiuzhaigou Mw6.5 earthquake in China on 8 August 2017, with their spatial distribution analyses

On August 8, 2017, a magnitude Mw6.5 (Ms7.0) earthquake occurred in Jiuzhaigou County, Aba Prefecture, in the northern part of Sichuan Province, China, with a focal depth of 20 km and an epicenter located at (33.2°N, 103.8°E). Due to the significant magnitude of the earthquake, a large number of coseismic landslides were triggered. Despite previous research conducted by experts on the landslides caused by the Jiuzhaigou earthquake, the actual number of landslides has been severely underestimated in the previously published papers. Through field surveys and visual interpretation of high-resolution remote sensing images before and after the mainshock, we have established a detailed inventory of earthquake-induced landslides. The results indicate that the event caused a minimum of 9428 landslides covering a total area of 18.82 km2. These landslides are mainly distributed in the IX intensity area of the earthquake. The landslides mainly consist of medium-sized landslides and debris flows. They predominantly occur in areas with an altitude ranging from 2600 m to 3600 m, with slopes greater than 30° and facing east and southeast. The Lower Carboniferous and Middle Carboniferous formations are more prone to triggering landslides, and landslides are more concentrated within 1 km of roads and in forested areas. Additionally, as the distance from roads and the epicenter increases, the values of LAP and LND decrease, indicating a positive correlation between the two. There are more landslides within 2 km from the fault and within a range of 6 km–9 km from the epicenter. In conclusion, this study provides a comprehensive landslide inventory with broader coverage and increased accuracy. It also conducts a comprehensive analysis of the spatial distribution patterns of landslides. This contributes to a deeper understanding of the causes of coseismic landslides and further research on the impact of landslides in affected areas.

Landslide mapping based on a hybrid CNN-transformer network and deep transfer learning using remote sensing images with topographic and spectral features

Landslides frequently cause serious property damage and casualties. Therefore, it is crucial to have rapid and accurate landslide mapping (LM) to support post-earthquake landslide damage assessment and emergency rescue efforts. Many studies have been conducted in recent years on the application of automatic LM methods using remote sensing images (RSIs). However, existing methods face challenges in accurately distinguishing landslides due to the problems of large differences in features and scales among landslides, as well as similarities among different ground objects in optical RSIs. Here, we propose a semantic segmentation model called SCDUNet++, which combines the advantages of convolutional neural network (CNN) and transformer to enhance the discrimination and extraction of landslide features. Then, we constructed a multi-channel landslide dataset in the Luding and Jiuzhaigou earthquake areas using Sentinel-2 and NASADEM data. We evaluated the performance of SCDUNet++ on this dataset. The results showed that SCDUNet++ can extract and fuse spectral and topographic information more effectively. Compared with other state-of-the-art models, SCDUNet++ achieved the highest IoU and F1 score in all four test areas. In addition, the models achieved significant improvements in mapping the landslides of the Jiuzhaigou area after knowledge transfer and fine-tuning. Compared with direct prediction, eight models, namely DeepLabv3+, Segformer, TransUNet, SwinUNet, STUNet, UNet, UNet++, and SCDUNet++, demonstrated improvements in IoU ranging from 8.33% to 27.5% and F1 from 6.58% to 23.67% after implementing deep transfer learning (DTL). This finding highlights the significant practicality of using DTL for cross-domain LM in data-poor areas.

Improved seismic intensity measures and regional structural risk estimation models

Seismic intensity measures are essential quantitative parameters for evaluating and predicting engineering structures’ seismic risk and vulnerability. Various regional earthquake analysis and vulnerability models with evaluation and prediction capabilities have been developed using macroseismic and instrumental intensity measures. These results have significant positive implications for evaluating large-scale regional earthquake resilience and predicting earthquake risks. However, the traditional determination of seismic intensity measures is based on given seismic station detection data and macroscopic intensity standards, and the developed seismic risk models are relatively outdated, resulting in relatively low accuracy in assessing the vulnerability of regional structures. This paper proposed an improved instrumental intensity calculation equation and updated the model using 216,000 acceleration records monitored by nine typical seismic stations in China during the Jiuzhaigou earthquake on August 8, 2017. The proposed calculation model was combined with the latest version of China’s macrointensity standard, and undamaged building samples were sufficiently considered. The vulnerability level of 1,499 buildings surveyed in the Jiuzhaigou earthquake was assessed. This paper innovatively proposed a membership index calculation model to estimate the impact relationship between different vulnerability levels. A comparative curve model was generated with the updated vulnerability databases of four typical regional structures, considering different vulnerability membership indices. Using the improved seismic intensity measures and nonlinear regression model, an improved vulnerability fitting model was developed to assess the seismic risk of regional structures. The model was analysed and verified using field survey data from the Jiuzhaigou earthquake.

Comparison of Emergency Management Strategies for Nosocomial Infections Between Two Earthquakes

Aim: Based on the experience of the 5.12 Wenchuan Earthquake and the 8.8 Jiuzhaigou Earthquake, the emergency management strategies for nosocomial infections were compared between the two earthquakes. The experience shared in the present study provides a guideline for the emergency medical rescue of future earthquake. Methods: The patients involved in this study were those injured in the earthquake and admitted to the hospital for treatment. As an earthquake relief center, the hospital participated in the emergency rescue work of the two recent major earthquakes in western China. Review analysis was carried out in the hospital’s infection control experience adopted in the two major earthquakes. It was emphasized that, targeting the characteristics and difficulties in the prevention and control of nosocomial infection, different innovative infection control emergency strategies were adopted by the frontline disaster relief hospitals, under the special circumstances and medical conditions in an earthquake disaster. Results: According to the different focus of infection control in the two earthquakes, different hospital infection control strategies were adopted, and the incidence of nosocomial infections was effectively controlled.

Rapid Emergency Response Assessment of Earthquake-Induced Landslides Driven by Fusion of InSAR Deformation Data and Newmark Physical Models

Strong earthquakes induce a large number of secondary disasters, such as landslides, which bring serious challenges to post-disaster emergency rescue, and the rapid and accurate assessment of earthquake-induced landslide disasters is crucial for post-earthquake emergency rescue. This research aims to propose an emergency assessment model that is suitable for post-earthquake landslides, specifically targeting the first 72 h after an earthquake for emergency rescue guidance. The model combines remote sensing technology and the Newmark physical mechanics assessment model to form the InSAR Data–Newmark Physical Fusion Driver Model (IDNPM), which comprehensively considers the dynamic deformation of the ground surface and geological features. To validate the predictive performance of the IDNPM, the model is applied to the 5 September 2022 Luding earthquake event and the 8 August 2017 Jiuzhaigou earthquake event. The landslide qualitative evaluation, confusion matrix and Receiver Operating Characteristic (ROC) curve are utilized for quantitative assessment. The results show that the IDNPM can effectively reduce the false negative and false positive errors in landslide prediction by utilizing the SAR deformation information, and to a certain extent, it accounts for the dependence of the Newmark model on the accuracy of empirical formulas and geotechnical parameters. For the Luding earthquake event, the IDNPM shows an accuracy improvement of 10.296% compared to the traditional Newmark model. For the Jiuzhaigou earthquake event, there is also an improvement of 3.152%, with a promising generalization performance. The simplicity and ease of operation in constructing the model are accompanied by high reliability and accuracy. The research findings provide essential references for the development of post-earthquake landslide emergency prediction models and offer robust data support for emergency rescue and recovery efforts in earthquake-stricken areas in the future.

Perceived parental depression and PTSD in adolescents: mediating roles of attachment insecurity and coping style

ABSTRACT Background: Earthquakes can cause negative psychological states in adolescents and their parents, such as post-traumatic stress disorder (PTSD) and depression. Previous cross-sectional studies have shown that adolescents who perceive parental depression after an earthquake are more likely to experience PTSD. However, the mechanism underlying this process remains unclear and previous studies have not examined the association between these factors. Objective: The current study explored the mediating roles of attachment insecurity and coping style in the association between perceived parental depression and PTSD in adolescents after the Jiuzhaigou earthquake. Method: In total, 391 participants completed follow-up questionnaires at 12, 21, and 27 months (T1–T3, respectively) after the Jiuzhaigou earthquake. Results: T1 perceived parental depression was not a direct predictor of T3 PTSD in adolescents. In the full mediation model, T1 perceived parental depression predicted T3 PTSD in adolescents indirectly via T2 attachment insecurities through T2 emotion-focused coping. Conclusion: The findings highlight that attachment insecurity and emotion-focused coping mediated the relationship between perceived parental depression and PTSD in adolescents following an earthquake. To alleviate PTSD in adolescents, efforts should be directed toward reducing perceived parental depressive symptoms and emotion-focused coping, and promoting the formation of secure attachment styles.

Comprehensive evaluation of natural landscapes before and after earthquake based on GST-FAHP-GIS analytical method: A case study of Jiuzhaigou Nature Reserve, China

The Jiuzhaigou earthquake caused severe damage to its natural landscape. The seismic-induced changes in landscapes represent a complex and diverse process. In order to creatively assess the extent of earthquake-induced landscape destruction and study the focal points of landscape restoration, this study proposes a comprehensive framework system that integrates multiple indicators and methods to evaluate the impact of earthquakes on the Jiuzhaigou scenic area. In comparison to previous single-method studies, our framework system offers a more holistic explanation and predictive capacity regarding the trends and characteristics of post-earthquake landscape changes. Within this approach, we amalgamate Grey Statistical Technique (GST), Fuzzy Analytic Hierarchy Process (FAHP), Delphi Technique, and Geographic Information System (GIS). By selecting 24 landscape evaluation indicators, we establish five levels of landscape assessment, ranging from broad to fine-grained. Areas with higher visibility sensitivity are categorized as high visual sensitivity regions, indicating the need for focused restoration efforts. Based on the degree of landscape visual sensitivity, we introduce four visual sensitivity evaluation factors: slope gradient, slope aspect, relative distance, and vegetation coverage. The result demonstrates that the overall landscape grade of Jiuzhaigou decreased by 15% after the earthquake; the aesthetic indicator of the landscape within geological hazard experienced the greatest decline, decreasing by 73.3%, while the landscape's resilience indicator showed relatively minor changes. Notably, regions with high visual sensitivity exhibit a distinctive “Y”-shaped distribution pattern, with 56.8% of environmental hazard points possessing the highest visual sensitivity. The study also underscores the concentrated presence of geological hazard points and high visual sensitivity between the Nuorilang Waterfall and the scenic area entrance, necessitating prioritized restoration efforts in this zone. This research innovatively provides a method for assessing the damage caused by earthquakes to natural landscapes, and it holds significant guiding implications for relevant national decision-making bodies in policy formulation and implementation.

In-situ testing for characterizing the landslide deposit in Jiuzhaigou cultural heritage site after the 2017 earthquake

The 2017 Jiuzhaigou earthquake caused numerous landslide masses in the Jiuzhaigou cultural heritage site, leading to frequent surface mass movements and affecting the hydrological landscape. This was the first time a strong earthquake hit the heritage site in China, making it an important area for ecological geological environment protection and restoration research. To understand the influence of slope runoff erosion on the activation of landslide accumulations, this study examined remote sensing images from 2017 to 2020 to investigate the geological disaster, while field scour tests were conducted to study the rainfall seepage, mass erosion, and migration.The results indicated that the steep-sloped landslide deposits in the heritage site contain a high content of fine grains and good permeability.

Automatic Detection of Forested Landslides: A Case Study in Jiuzhaigou County, China

Landslide detection and distribution mapping are essential components of geohazard prevention. For the extremely difficult problem of automatic forested landslide detection, airborne remote sensing technologies, such as LiDAR and optical cameras, can obtain more accurate landslide monitoring data. In practice, however, airborne LiDAR data and optical images are treated independently. The complementary information of the remote sensing data from multiple sources has not been thoroughly investigated. To address this deficiency, we investigate how to use LiDAR data and optical images together to develop an automatic detection model for forested landslide detection. First, a new dataset for detecting forested landslides in the Jiuzhaigou earthquake region is compiled. LiDAR-derived DEM and hillshade maps are used to mitigate the influence of forest cover on the detection of forested landslides. Second, a new deep learning model called DemDet is proposed for the automatic detection of forested landslides. In the feature extraction component of DemDet, a self-supervised learning module is proposed for extracting geometric features from LiDAR-derived DEM. Additionally, a transformer-based deep neural network is proposed for identifying landslides from hillshade maps and optical images. In the data fusion component of DemDet, an attention-based neural network is proposed to combine DEM, hillshade, and optical images. DemDet is able to extract key features from hillshade images, optical images, and DEM, as demonstrated by experimental results on the proposed dataset. In comparison to ResUNet, LandsNet, HRNet, MLP, and SegFormer, DemDet obtains the highest mean accuracy, mIoU, and F1 values, namely 0.95, 0.67, and 0.777. DemDet is therefore capable of autonomously identifying the forest-covered landslides in the Jiuzhaigou earthquake zone. The results of landslide detection mapping reveal that slopes along roads and seismogenic faults are the most crucial areas requiring geohazard prevention.

Characteristics of Seismogenic Dust Particles from a Mountain and Their Significance for Paleoseismic Records in a Tufa Section: A Case Study of Jiuzhaigou, China

The sedimentary characteristics of the special interlayer contained in the section of the Sparkling Lake dam revealed by the 8 August Jiuzhaigou earthquake in 2017 are obviously different from the tufa in the dam body, and they are considered to be historic flood relics. Based on the study of the particulate matter from the 8 August Jiuzhaigou earthquake, this study combined comparative petrographic, mineralogical, geochemical, and chronological studies of the special interbedded sediments of the Sparkling Lake dam with other genetic samples to obtain homology information and genetic links and to explore the tufa depositional dynamics and earthquake disaster subsidence. The paleoseismic benefit in the discontinuity layer was explored, and the paleoseismic information embedded in the profiles was extracted, providing a new idea for reconstructing the paleoseismic events in the tufa deposition sequence. According to X-ray diffraction, laser particle size analysis, and scanning electron microscope experiments, the particulate matter in the Jiuzhaigou mountains has its own specific mineralogical characteristics. The mineral composition of the particulate matter is basically calcite. The particle size is large, and single particles are mostly angular and subangular. The fracture morphology observed under the single-particle microscope was uneven, showing the characteristics of the dust caused by earthquake disasters. The geochemical analysis data show that the distribution patterns of rare earth elements in different types of particulate matter in the same area have similar characteristics. Moreover, an analysis of the elements Rb, Sr, and Ba shows that the particulate matter in Jiuzhaigou has a strong correlation (R2 = 0.9941), indicating the stability and uniformity of the material source. The source of the particulate matter was limestone of the carbonate strata from the Devonian to the Triassic. Combined with mineralogy and grain size morphology, the interbedded particles in the tufa depositional profile have the potential application of paleoseismic archives to record extreme seismic events. According to the chronological data of the special interlayer sediments in the tufa dam body, the paleoseismic age is inferred to be 1220 ± 30 BP. Therefore, the tufa bedding can be related to the paleoearthquake, and the special interlayer of the tufa section can be used to reconstruct paleoearthquakes.

Perceived parental depression, intrusive rumination, and internalizing problems: A three-wave longitudinal study in adolescents.

Research suggests that perceiving parental depression elicits internalizing problems in adolescents, but certain studies have indicated that adolescents' internalizing problems also increase their perception of parental emotion. To further investigate the inconsistent findings about the nature of this relationship, the current study used longitudinal data to examine the causal association between adolescents' internalizing problems and the parental depression they perceived, as well as the role of intrusive rumination in the relationship. Method: In this longitudinal study, 392 adolescents who experienced the catastrophic Jiuzhaigou earthquake in 2017 were surveyed at three time points after the earthquake: 12 months (T1), 21 months (T2) and 27 months (T3). A cross-lagged panel model was used to carry out the data analysis. Results: Mutual cause-and-effect relationships were found between intrusive rumination and both perceived parental depression and internalizing problems, respectively; a unilateral causal relationship in which internalizing problems positively predicted perceived parental depression was also found. In addition, internalizing problems predicted perceived parental depression via the mediating role of intrusive rumination; similarly, intrusive rumination predicted perceived parental depression via internalizing problems. Conclusions: Internalizing problems were a risk factor for perceived parental depression, and intrusive rumination played an important role in the relationship between internalizing problems and perceived parental depression.

Seismic potential in the southeastern margin of Bayanhar block after 2017 Jiuzhaigou earthquake, China – insight from stress evolution and earthquake probability estimation

The southeastern margin of the Bayanhar block (BHB) is located at the junction of the Bayanhar block and the southern China block (SCB). Strong earthquakes occurred frequently in this region because of the intensive tectonic movement. The stress changes caused by strong earthquakes and the observed background seismicity can be used to forecast strong earthquake probability in this region. Therefore, we construct a 3D (three Dimension) viscoelastic finite element model that includes the complex fault system, inhomogeneous medium, tectonic stress, and gravity to simulate the evolutionary stress field when using the observed Global Positioning System (GPS) vectors as the boundary conditions. In addition, we calculate the strong earthquake probability based on the calculated stress and a Poisson probability distribution. The stress evolution result shows the Xianshuihe fault (XSHF), the Huya fault (HYF) and the southern segment except for the Yaan County of Longmenshan fault (LMSF) are located in the stress enhancement area indicating high seismic potential. However, the Minjiang fault (MJF) is located in the stress shadow area. After the Lushan and Jiuzhaigou earthquakes, there is still a high possibility of strong earthquakes in the MJF, HYF, and LMSF zone, according to the strong earthquake probability result.

Causes of the discordance in the south-north distribution of the 2017 Jiuzhaigou earthquake sequences

Causes of the discordance in the south-north distribution of the 2017 Jiuzhaigou earthquake sequences

The reciprocal relations among self-compassion, posttraumatic stress disorder, and posttraumatic growth among adolescents after the Jiuzhaigou earthquake: a three-wave cross-lagged study

Background Previous cross-sectional studies have examined the relationship between self-compassion, post-traumatic stress disorder (PTSD), and post-traumatic growth (PTG). But no study has tested whether self-compassion is a cause or a consequence of PTSD, PTG, or both. Aims The cross-lag analysis was used to examine the reciprocal effects among self-compassion, PTSD, and PTG. Method We used data from 244 adolescents who had experienced earthquakes. We assessed self-compassion, PTSD, and PTG via self-report measures after the earthquake in Jiuzhaigou, as well as 6 and 12 months later. Results Cross-lagged analyses indicated that positive self-compassion could significantly predict subsequent PTSD and PTG. Meanwhile, PTSD and PTG also predicted later positive self-compassion. Negative self-compassion at T1 increased PTSD at T2, and neither PTSD nor PTG significantly predicted subsequent negative self-compassion. In addition, negative self-compassion at T1 significantly predicted positive self-compassion at T2, while positive self-compassion at T2 significantly predicted negative self-compassion at T3. Conclusions Positive self-compassion is a protective factor of post-traumatic psychological response, and it is maintained for a long time, while negative self-compassion may aggravate the negative psychological outcomes of adolescents in the early stage of experiencing traumatic events. In addition, positive and negative self-compassion can influence each other over time.

The reciprocal relations among self-compassion, and depression among adolescents after the Jiuzhaigou earthquake: A three-wave cross-lagged study.

Previous cross-sectional studies have examined the relationship between self-compassion and depression. Although it is often implicitly assumed that self-compassion may increase the vulnerability of an individual to depression, only a few studies have assessed whether self-compassion is a cause or a consequence of depression or both. To examine such reciprocal effects, we assessed self-compassion and depression via self-report measures. At the baseline assessment (Time 1, T1), 450 students (M = 13.72, SD = 0.83, 54.2% females) participated 10 months after the Jiuzhaigou earthquake. We reassessed the T1 sample after 6- and 12-month intervals. At Time 2 (T2) assessment, 398 (56.0% female participants) of the Wave 1 participants were retained, and at Time 3 (T3) assessment, 235 (52.5% female participants) of the T1 and T2 participants were retained. Cross-lagged analyses indicated that positive self-compassion could significantly reduce subsequent depression. However, depression did not significantly predict later positive self-compassion. Negative self-compassion at T1 increased depression at T2, but negative self-compassion at T2 did not significantly predict depression at T3. In addition, positive self-compassion significantly reduced subsequent negative self-compassion. Positive self-compassion appears to protect adolescents against depression and maintain this protection over time, whereas negative self-compassion may worsen depression in adolescents during the initial stages of traumatic events. Additionally, positive self-compassion may decrease the level of negative self-compassion.

Semantic Segmentation Model for Wide-Area Coseismic Landslide Extraction Based on Embedded Multichannel Spectral–Topographic Feature Fusion: A Case Study of the Jiuzhaigou Ms7.0 Earthquake in Sichuan, China

The rapid and accurate extraction of wide-area coseismic landslide locations is critical in earthquake emergencies. At present, the extraction of coseismic landslides is mainly based on post-earthquake site investigation or the interpretation of human–computer interactions based on remote sensing images. However, the identification efficiency is low, which seriously delays the earthquake emergency response. On the basis of the available multisource and multiscale remote sensing data, numerous studies have been carried out on the methods of coseismic landslide extraction, such as pixel analysis, object-oriented analysis, change detection, and machine learning. However, the effectiveness of coseismic landslide extraction was low in wide areas with complex topographic and geomorphic backgrounds. Therefore, this paper offers a comprehensive study of the factors influencing coseismic landslides and researches rapid and accurate wide-area coseismic landslide extraction methods with multisource remote sensing and geoscience technology. These techniques include digital elevation modeling (DEM) and its derived slopes and aspects. An embedded multichannel spectral–topographic feature fusion model for coseismic landslide extraction based on DeepLab V3+ is proposed, and a knowledge-enhanced deep learning information extraction method integrating geological knowledge is formed. Using the Jiuzhaigou Ms7.0 earthquake (seismic intensity VIII) in Sichuan Province, China, a comparison of landslide extraction models and strategies is carried out. The results show that the model proposed in this paper achieves the best balance in the accuracy and efficiency of wide-area extractions. Using multiple feature data of coseismic landslides, the problem of mixed pixels is solved. The rate of the misidentification of landslides as clouds, snow, buildings, and roads is significantly lower than in other methods. The identified landslide boundaries are smoother and more accurate, and the connectivity is better. Compared with other methods, ours can more accurately eliminate landslides not triggered by the Jiuzhaigou earthquake. While using the image block strategy to ensure extraction efficiency, it also improves the extraction accuracy of wide-area coseismic landslides in complex backgrounds.