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The earthquake and its secondary geological disasters have some long-term effects on vegetation recovery, and the spatiotemporal differentiation of vegetation coverage in earthquake-stricken areas are often not valued by researchers. Beichuan County in Sichuan Province is the worst-hit area during Wenchuan earthquake, tracking the vegetation coverage in this disaster area can provide feedback for post-earthquake ecological restoration. Based on remote sensing (RS) and Geographic information system (GIS), this paper relies on dimidiate pixel model (DPM) and vegetation coverage transfer matrix to explore the vegetation coverage of the earthquake worst-hit area. The results show that the overall level of vegetation coverage in Beichuan county is high, and earthquakes are dominant in the areas with high vegetation coverage. Besides, earthquake can do most severe damages to the areas with high vegetation coverage, followed by medium damage impacting to the areas with medium vegetation coverage, but the damage of other areas are relatively small. From 2007 to 2020, the average vegetation coverage in Beichuan County decreased from 0.835 in 2007 to 0.755 in 2008, then gradually recovered to 0.826 in 2020, experiencing a sharp decline and then a steady increase. The recovery of vegetation coverage is improved over time, but the spatiotemporal differences of the recovery process are obvious. Elevation has the largest driving force on vegetation coverage (q = 0.569, p less than 0.001), followed by temperature and rainfall; the interaction between factors has a significant increasement in the spatial differentiation of vegetation coverage, indicating that vegetation coverage is a combination result of multiple factors. The research findings can help construct the scientific management of the eco-environment in the disaster area, it can also help builda medium to long-term ecological restoration plan according to the variation in vegetation coverage. [ABSTRACT FROM AUTHOR]

• Abrupt high δ13C -CO2-DIC in the hot springs occurred before earthquakes. • The carbon isotopic spikes are caused by fractionation due to tectonic activity. • The δ13C -CO2-DIC of hot springs changed markedly before and after the earthquakes. • The elevated carbon isotopic levels may be a signal for the subsequent earthquakes. Geothermal springs are controlled by deep faults, which are the major causes of earthquakes. A series of geochemical indicators of hot springs, such as carbon dioxide content (CO 2), carbon isotope and noble gas isotope etc., have been used for earthquake monitoring and early warning at home and abroad. Given that previous studies have failed to establish an inevitable link between the carbon isotopic variations of hot springs and seismic activities, in this study, mass spectrometry techniques were used to analyze the carbon isotopes of gaseous CO 2 and noble gas isotope in hot springs, thermal water, and newly formed travertines from western Sichuan before and after an Ms 4.8 earthquake (i.e., 7.14 earthquake). The results showed that the carbon isotope level of the hot spring system increased significantly before the earthquake, i.e., δ13C CO2 = -2‰ to 3.3‰, δ13C DIC = 2.2‰ to 6.6‰ (DIC = dissolved inorganic carbon in geothermal water), R/R A = 0.90–1.83, and δ13C travertine = 1.76‰ to 5.75‰. These indicators decreased markedly 1 week after the earthquake, whereas the helium isotope ratio of R/R A did not exhibit a noticeable change (δ13C CO2 = -3.9‰ to 0.4‰, δ13C DIC = 1.6‰ to 4.1‰, R/R A = 0.68–1.87). Combined with previous thermodynamic calculation and experimental results on carbon isotope fractionation of the CO 2 –calcium carbonate system, we suggest that the high carbon isotope characteristics of hot springs are caused by carbon isotope fractionation following the release of CO 2 from fault carbonate decomposition owing to tectonic activity, not by a mixture of different carbon sources. A high carbon isotopic delivery path in geothermal water, from released CO 2 to DIC and then to travertine, was observed. The new mechanism proposed in this paper reveals the intrinsic relations between the changes of the hot spring geochemical properties and deep tectonic activities, and the variations of these geochemical parameters before and after earthquakes may assist in capturing seismic precursors. A post-peak earthquake warning model has thus been preliminarily proposed, however, long-term monitoring data are required to reduce some uncertainties. [ABSTRACT FROM AUTHOR]

Although there is a growing body of literature on the COVID-19 pandemic and the implemented strategies that have imposed additional risks in tackling emergencies by increasing socio-economic vulnerabilities, studies on human evacuation behavior during lockdowns are lacking. This paper contributes to evacuation and emergency research by examining seismic evacuation decision-making by surveying the areas affected by the Luding earthquake on 5 September 2022, when most parts of Sichuan province were experiencing strict pandemic restrictions. Using these data and per the emergency evacuation decision-making mechanism, we developed six hierarchical series of logistic regression models. Our major results suggest that 1) Socio-demographic parameters have shown different correlations with the study's dependent variables in each stage of those hierarchical models, 2) Respondents at home at the time of the earthquake were more likely to identify the earthquake risk than those who stayed outdoors; the former group showed less willingness to evacuate, 3) Rural residents have perceived higher earthquake risks than urban residents, and 4) Loss of job, change in income due to COVID-19 restrictions, and difficulty accessing daily supplies during the lockdown affected the residents' risk assessment and evacuation decision-making negatively. Insights into these aspects are expected to contribute to a better understanding of evacuation behavior during double disasters by modifying emergency response regulations and providing the residents with information about emergencies during pandemic restrictions. • Examining Emergency Decision-Making Mechanism (EDMM) during a vast Covid-19 epidemic lockdown • Epidemic prevention regulations and access to earthquake information significantly affected the residents' earthquake risk perception, evacuation decision-making, and risk identification, respectively. • Access to disaster information has played a critical role in risk recognition. • Rural residents felt more vulnerable to the earthquake than urban residents. [ABSTRACT FROM AUTHOR]

The terrain along the Tibet Railway is primarily steep mountains and valleys with complex geological structures. The occurrence of the Mainling earthquake indicates that the intensity of seismicity in the eastern structural junction area has strengthened, and the potential risk of earthquake-induced disasters such as landslides and debris flows is also increasing. The earthquake engineering community generally recognizes that the prediction of near-field strong ground motion can reduce the disasters caused by future large earthquakes and can guide earthquake prevention, disaster reduction and seismic design along the Sichuan–Tibet railway. The M6.9 Mainling earthquake affected the stability of Sedongpu Glacier near the epicentre. A glacier avalanche occurred under the coupling of multiple factors, including post-earthquake and precipitation events. The waveform and response spectra of a strong earthquake are key input parameters for investigating the seismic triggering mechanisms of landslides and glacier avalanches and accurately reproducing the dynamics of such disasters. In this paper, the concept of the equivalent Green's function is introduced, and various source parameter uncertainties are fully considered. The waveform and response spectra of the Mainling earthquake at Sedongpu Glacier are simulated, and the most likely seismic waveform is comprehensively assessed from multiple perspectives. This work can provide reliable acceleration waveform input for the analysis of ground motion damage and the study of earthquake-induced disaster mechanisms at important locations along the Sichuan–Tibet railway and provide a scientific reference for earthquake prevention and disaster reduction along the railway. • The concept of equivalent Green's function is proposed. • The waveform containing many practical and complex information for seismic damage analysis • It expands the application range of empirical Green function method. • It could help to study the mechanism of earthquake induced geological hazards. [ABSTRACT FROM AUTHOR]

The 20 April 2013 Lushan earthquake with a magnitude of M s 7.0 induced widespread landsliding along the southern segment of the Longmen Shan fault zone. This paper presents a preliminary analysis of the characteristics of these co-seismic landslides triggered in an area of 13,323 km 2 . A total of 3810 individual landslide points were identified through an intensive field investigation and from digital aerial photographs taken after the Lushan earthquake. To facilitate the more detailed analysis on the areal frequency of co-seismic landslides as polygons, we selected an area of 732 km 2 around the epicenter that had the highest density of co-seismic landslides. The sizes of the landslides are, in general, much smaller than those that were triggered by the 2008 Wenchuan earthquake. The most common types of landslides in the study area were rock falls and shallow, disrupted landslides from steep slopes, which typically involved the top few meters of weathered bedrock and colluvium. In this study, the relation between the landslide distribution and five main factors was analyzed: fault proximity, epicenter proximity, peak ground acceleration (PGA), slope angle, and lithology. Our study results show that the landslide concentration tends to decrease with distance from the seismogenic fault, and the highest landslide concentration is located within a distance of 5 km. Regarding the distance to the epicenter, the largest landslide density is found at a distance between 5 and 10 km. The landslide concentration also increases with increasing slope angle until a maximum for the slope class 45°–55°. In addition, results show that slopes consisting of deeply weathered and fractured sandstones and mudstones were the most susceptible to co-seismic landslides. In this study, we found that the location of the hanging wall and the footwall of the fault did not seem to have an important effect on the landslide distribution. This is in contradiction to what has been reported in other earthquake areas such as the Wenchuan area. [ABSTRACT FROM AUTHOR]

Earthquake relic is a unique geoheritage. At present, the research on earthquake relic geoparks or national parks is less domestic and overseas, while the protection, development and exploration are in its infancy. This paper, taking ‘Qingchuan Earthquake Relic Geopark’ (QERG) as an example, analyses the characteristics of the geopark, and explores the development model, in order to provide reference for the earthquake relic geopark and national park development domestic and overseas. [ABSTRACT FROM AUTHOR]

• Integration of GPS and strong-motion data for more accurate coseismic displacement. • Novel model of fusing GPS and strong-motion data with baseline shift correction. • Better performance of the integrated model shown in 2017 Jiuzhaigou earthquake. Near-field coseismic displacement information is very important for seismological research. High-rate GPS and seismic sensors are mutually advantageous for capturing earthquake-induced coseismic displacements. The combination of them can help to improve the near-field observation results. In this paper, we propose a combined model of fusing GPS and strong-motion records by taking into consideration the corrections of baseline shift, and then apply a multi-rate Kalman filter to analyze the collocated GPS and strong-motion data. The performance of the proposed approach is validated using 1 Hz GPS data and 200 Hz strong-motion data collected within 150 km from the epicenter during the Ms 7.0 Jiuzhaigou earthquake occurred on August 8, 2017 in Sichuan Province, China (33.20°N, 103.82°E). It shows that the GPS displacements based on the Epoch-relative Positioning Approach well reflect the instantaneous motion state of stations during the earthquake, indicating that the maximum instantaneous displacement of SCJZ station is 3.2 cm in north and −2.0 cm in east, and the permanent displacement of SCSP station is −0.6 cm in north. More accurate and reliable broadband displacements less than 1 cm are obtained by the combination model, which provide the full spectrum of the seismic motion. The integrated displacements have more characteristics of strong-motion results in the early stage of the earthquake with less noise, and in the strong shaking period, tightly bound by the GPS displacements, the integrated displacements will not diverge, and thus accurately record the long-period information of seismic waves. [ABSTRACT FROM AUTHOR]

In 2008, the Wenchuan earthquake triggered many large landslides with rapid movement and long runouts, resulting in a great number of casualties. Although there have been many studies of the geographical features and initiation mechanisms of some catastrophic landslides, the movement mechanisms for many remain unclear. In this paper, we present a case study of a large landslide (debris avalanche) triggered by the 2008 Wenchuan earthquake in the Donghekou area, Sichuan Province, China. We made detailed field surveys of the geographical features of the landslide and carried out subsurface investigations of the landslide deposits using microtremor array measurement and electrical resistivity tomography (ERT). Based on the observed surficial features, shear-wave velocity (Vs) profiles and 2D electrical resistivity profiles of the landslide deposits, we estimated the possible thickness of landslide deposits at different locations, and also analyzed the possible landsliding mechanisms. We inferred that this landslide resulted from retrogressive failures on the source area, and the displaced landslide materials underwent transitional spreading with further entrainment of debris along the travel path. Multiple mud waves might have been formed in the substrate soil layers along the travel path due to the entraining of landsliding materials, and the landsliding materials might have presented channelized movements, indicating that different parts may have moved at different speeds. This kind of transportation mechanism may provide information for elevating the numerical simulation of landsliding, and also for reuse of deposit area of large landslides. • Donghekou landslide is a debris avalanches resulted from retrogressive failures. • The boundary between the landslide deposits and bedrock had been identified. • Main surges with mud waves on the bottom of landslide material had been identified. • Channelized sliding may have occurred within the landsliding materials. [ABSTRACT FROM AUTHOR]

The increasing volume of seismic data from long-term continuous monitoring motivates the development of algorithms based on convolutional neural network (CNN) for faster and more reliable phase detection and picking. However, many less studied regions lack a significant amount of labeled events needed for traditional CNN approaches. In this paper, we present a CNN-based Phase-Identification Classifier (CPIC) designed for phase detection and picking on small to medium sized training datasets. When trained on 30,146 labeled phases and applied to one-month of continuous recordings during the aftershock sequences of the 2008 M W 7.9 Wenchuan Earthquake in Sichuan, China, CPIC detects 97.5% of the manually picked phases in the standard catalog and predicts their arrival times with a five-times improvement over the ObsPy AR picker. In addition, unlike other CNN-based approaches that require millions of training samples, when the off-line training set size of CPIC is reduced to only a few thousand training samples the accuracy stays above 95%. The deployment of CPIC takes less than 12 h to pick arrivals in 31-day recordings on 14 stations. In addition to the catalog phases manually picked by analysts, CPIC finds more phases for existing events and new events missed in the catalog. Among those additional detections, some are confirmed by a matched filter method while others require further investigation. Finally, when tested on a small dataset from a different region (Oklahoma, US), CPIC achieves 97% accuracy after fine tuning only the fully connected layer of the model. This result suggests that the CPIC developed in this study can be used to identify and pick P/S arrivals in other regions with no or minimum labeled phases. • Design and test a feasible CNN scheme for small to medium size training sets which is accurate and efficient compared to manual picking. • The aftershock dataset used in the test is known to be challenging for phase picking due to the existence of a large number of weak events and low signal-to-noise ratio. • The CNN structure we propose can be trained on the small training set (< 10, 000 labels) with 95% classiffication accuracy. • Deployment of the trained CNN is efficient: detection takes two hours and picking takes 12 hours to process 31 days of data on 14 stations. • Many weak events detected by the CNN but missed by manual pickings can be verified by a more complete catalog created via the matched filter approach. [ABSTRACT FROM AUTHOR]