The earthquake in Jiuzhaigou County of Northern Sichuan, China on August 8, 2017

Lei, Hao; Wang, Xiuling; Hou, Hang; Su, Linjian; Yu, Dai; Wang, Hao

doi:10.1007/s11069-017-3064-3

Cited by 27 publications

(9 citation statements)

References 26 publications

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“…The FR method is one of the most widely used approaches to assess the landslide susceptibility at a regional scale (Guo et al, 2015;Li et al, 2017;Mohammady et al, 2012), which is based on the observed spatial relationship between landslide locations and controlling factors (Lee and Pradhan, 2007;Poudyal et al, 2010). The assumption behind the FR is that future landslides will occur under similar environmental conditions as historical landslides (Guzzetti et al, 1999;Pourghasemi and Rahmati, 2018), and the susceptibility can be evaluated from the relationship between the controlling factors and the landslide occurrence locations (Zhu et al, 2014).…”

Section: Frequency Ratio (Fr)mentioning

confidence: 99%

“…Over the last decades, many approaches for landslide susceptibility mapping were proposed, among which the application of remote sensing associated with GIS modelling techniques became the most popular and effective method (Alexander, 2008;Carrara et al, 1991;Dai and Lee, 2002;Guzzetti et al, 1999;Lee, 2005;Mantovani et al, 1996;Mansouri Daneshvar, 2014;Xu et al, 2012a). The most commonly used methods for landslide susceptibility mapping include logistic regression (Ayalew and Yamagishi, 2005;Bai et al, 2010;Manzo et al, 2013;Ozdemir and Altural, 2013), weights of evidence (Althuwaynee et al, 2012;Regmi et al, 2010), analytical hierarchy process (AHP) (Kayastha et al, 2013;Komac, 2006;Mansouri Daneshvar, 2014;Yalcin, 2008), frequency ratio (FR) (Guo et al, 2015;Lee and Pradhan, 2007;Li et al, 2017;Mohammady et al, 2012), support vector machine (SVM) (Marjanović et al, 2011;Su et al, 2015), decision tree (Nefeslioglu et al, 2010;Saito et al, 2009) and artificial neural network (ANN) (Caniani et al, 2008;Catani et al, 2005;Conforti et al, 2014;Ermini et al, 2005;Pradhan and Lee, 2009). These methods have been proven capable of mapping the locations that are prone to landslides; however, some shortcomings still exist in these methods, which reduce the efficiency of these susceptibility methods when applied individually (Tien Bui et al, 2012;Umar et al, 2014).…”

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confidence: 99%

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GIS-based earthquake-triggered-landslide susceptibility mapping with an integrated weighted index model in Jiuzhaigou region of Sichuan Province, China

Zhang

et al. 2019

Nat. Hazards Earth Syst. Sci.

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Abstract. A Mw=6.5 earthquake struck the Jiuzhaigou region of Sichuan Province, China, at 21:19 LT on Tuesday, 8 August 2017, and triggered a large number of landslides. For mitigating the damages of earthquake-triggered landslides to individuals and infrastructures of the earthquake-affected region, a comprehensive landslide susceptibility mapping was attempted with an integrated weighted index model by combining the frequency ratio and the analytical hierarchy process approaches under a GIS-based environment in the heavily earthquake-affected Zhangzha town of the Jiuzhaigou region. For this purpose, a total number of 842 earthquake-triggered landslides were visually interpreted and located from Sentinel-2A images acquired before and after the earthquake at first, and then the recognized landslides were randomly split into two groups to establish the earthquake-triggered landslide inventory, among which 80 % of the landslides were used for training the integrated model and the remaining 20 % for validation. Nine landslide controlling factors were considered including slope, aspect, elevation, lithology, distance from faults, distance from rivers, land use–land cover, normalized difference vegetation index and peak ground acceleration. The frequency ratio was utilized to evaluate the contribution of each landslide controlling factor to landslide occurrence, and the analytical hierarchy process was used to analyse the mutual relationship between landslide controlling factors. Finally, the landslide susceptibility map was produced by using weighted overlay analysis. Furthermore, an area under the curve approach was adopted to comprehensively evaluate the performance of the integrated weighted index model, including the degree of model fit and model predictive capability. The results demonstrated the reliability and feasibility of the integrated weighted index model in earthquake-triggered landslide susceptibility mapping at a regional scale. The generated map can help engineers and decision makers assess and mitigate hazards of the earthquake-triggered landslides to individuals and infrastructures of the earthquake-affected region.

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Section: Frequency Ratio (Fr)mentioning

confidence: 99%

mentioning

confidence: 99%

GIS-based earthquake-triggered-landslide susceptibility mapping with an integrated weighted index model in Jiuzhaigou region of Sichuan Province, China

Zhang

et al. 2019

Nat. Hazards Earth Syst. Sci.

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“…The Jiuzhaigou Earthquake, which occurred on 8 August 2017 (Ms = 7.0, epicenter located at 33.2°N, 103.82°E) with a focal depth of 12.0 km in Jiuzhaigou County, Sichuan Province, China, was triggered by the reactivation of faults of the Longmenshan fault zone (Chen et al 2018;Lei et al 2018). The main shock of the Jiuzhaigou Earthquake started along the northwestern direction of the Huya fault (Fig.…”

Section: Study Areamentioning

confidence: 99%

Risk Identification of Seismic Landslides by Joint Newmark and RockFall Analyst Models: A Case Study of Roads Affected by the Jiuzhaigou Earthquake

Yue

Yin

et al. 2018

Int J Disaster Risk Sci

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Geological disasters are a great threat to people's lives and property. At present, it is difficult to evaluate quantitatively the cascading effects of regional geological disasters, and the development of new methods for such evaluation is much needed. In this study, the authors have developed a joint procedure that couples the Newmark model and the RockFall Analyst model based on a GIS platform in order to identify the impact of seismic landslides on roads. The new method effectively combines two processes-seismic landslide occurrence probability analysis and mass movement trajectory simulation. The permanent displacement derived from the Newmark model is used to identify potential source areas of landslides. Based on the RockFall Analyst model, the possible impact of mass movement on the roads can be simulated. To verify the reliability of the method, the landslides induced by the 2017 Jiuzhaigou Earthquake were taken as a case study. The results suggest that about 21.37% of the study area is at high risk of seismic landslides, and approximately 3.95 km of road sections are at extremely high risk of large landslides. The simulated area is consistent with the distribution of disasters revealed by post-earthquake remote sensing image interpretation and field investigation in existing studies. This indicates that the procedure, which joins the Newmark and RockFall models, has a high reliability for risk identification and can be applied to seismic landslide risk assessment and prediction in similar areas.

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“…The Ms7.0 Jiuzhaigou earthquake [ 1 ] in 2017 is the latest event ( Figure 1 ). This earthquake occurred on the east of the Eastern Kunlun fault belt of the eastern Bayan Har block, where the Tazang fault, Minjiang fault, and Huya fault converge toward together and is approximately 20 km [ 2 , 3 , 4 , 5 ] beneath the crust. This area is also considered to be the triple junction of the Minjiang tectonic belt, Longmenshan orogenic belt, and the West Qinling–Qilian orogenic belt.…”

Section: Introductionmentioning

confidence: 99%

Crustal Density Structure of the Jiuzhaigou Ms7.0 Earthquake Area Revealed by the Barkam–Jiuzhaigou–Wuqi Gravity Profile

Yang

Shen

Tan

et al. 2021

Sensors

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The Barkam–Jiuzhaigou–Wuqi gravity profile extends across the Jiuzhaigou Ms7.0 earthquake (in 2017) zone and passes through several historical big earthquakes’ zones. We have obtained Bouguer gravity anomalies along the profile composed of 365 gravity observation stations with Global Positioning System (GPS) coordinates, analyzed the observed data and inverted subsurface density structure. The results show that the Moho depth has a big lateral variation from southwest to northeast, which shallows from 57 km to 43 km with maximum variation up to 14 km within 800 km. The most acute depth change of the Moho is in the boundary region between the Bayan Har block and West Qinling–Qilian block. According to our analysis, it is related to the eastward movement of the Bayan Har block. There are three main pieces of evidence that support it: (1) Density is higher in the east of the Bayan Har block and smaller in the west, which is the same as seismic activity; (2) Two thin low-density layers exist in the upper and middle crust of the Bayan Har block, which may promote inter-layer slip and the Jiuzhaigou Ms7.0 earthquake occurred in the boundary area of the two low-density layers, where the crustal density and Moho surface fluctuate sharply; (3) the GPS velocity field in the southwestern part gravity profile is significantly larger than that of the northeastern part, which is consistent with the density structure. Our studies also suggest that the large undulation of the Moho prevents the movement of the Bayan Har block, and strain is prone to accumulate here. The dynamic background analysis of the crust in this area indicates that the Moho surface uplifts in the West Qinling–Qilian block, which decelerates the eastern migration of material on the Qinghai–Tibet Plateau, and leads to the weak tectonic activity of the north part of the Bayan Har block.

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The earthquake in Jiuzhaigou County of Northern Sichuan, China on August 8, 2017

Cited by 27 publications

References 26 publications

GIS-based earthquake-triggered-landslide susceptibility mapping with an integrated weighted index model in Jiuzhaigou region of Sichuan Province, China

GIS-based earthquake-triggered-landslide susceptibility mapping with an integrated weighted index model in Jiuzhaigou region of Sichuan Province, China

Risk Identification of Seismic Landslides by Joint Newmark and RockFall Analyst Models: A Case Study of Roads Affected by the Jiuzhaigou Earthquake

Crustal Density Structure of the Jiuzhaigou Ms7.0 Earthquake Area Revealed by the Barkam–Jiuzhaigou–Wuqi Gravity Profile

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