Coseismic landslides induced by the 2018 M w 6.6 Iburi, Japan, Earthquake: spatial distribution, key factors weight, and
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Ming Chang I Yu Zhou I Chao Zhou I Tristram C. Hales
Coseismic landslides induced by the 2018 Mw 6.6 Iburi, Japan, Earthquake: spatial distribution, key factors weight, and susceptibility regionalization
Abstract This research aims to explore detailed spatial distribution characteristics, identify key factors weight, and establish an accurate susceptibility regionalization model of coseismic landslides by the Mw 6.6 Iburi, Japan, earthquake sequence of 6 September 2018. Based on the remote sensing interpretation database, 5,977 individual coseismic landslides were delineated, which occupies an area of about 15.26 km2. The relationship between eight key factors and spatial distribution of coseismic landslides were precisely analyzed by the landslide area density (LAD) and landslide point density (LPD) curves. In order to obtain the weight of eight key factors more accurately, this paper compares the similarities and differences of the two curves in the earthquakes with similar magnitude, which was included the Lushan and Jiuzhaigou earthquake in China. According to the weight of eight key factors, the improved weight of evidence method was used to produce the susceptibility regionalization map of coseismic landslides in the Iburi earthquake. The results show that the improved weight of evidence method has better precision and can provide a scientific coseismic landslides susceptibility regionalization map for disaster prevention and mitigation in the Iburi earthquake. Keywords Coseismic landslides . Spatial distribution . Key factors weight . Susceptibility regionalization Introduction Detailed and accurate inventories are of great significance in many aspects of research on coseismic landslides, such as initiation mechanisms, motion type, and risk assessment of landslides. They are also useful for acquainting characteristics of seismogenic faults, formation lithology, and peak acceleration of earthquakes. Therefore, this research direction has attracted much attention in recent years. Some researchers have carried out database construction for different earthquake zones, such as the 2017 Jiuzhaigou, China Mw6.5 (Chen et al. 2018; Fan et al. 2018), 2016 Kumamoto, Japan Mw7.0 (Xu et al. 2018; Miyazawa 2018), 2015 Gorkha, Nepal Mw7.8 (Kargel et al. 2016; Xu et al. 2017), 2014 Ludian, China Mw6.1 (Chen et al. 2015; Hu et al. 2017), the 2013 Minxian, China Mw5.9 (Tian et al. 2017), 2013 Lushan, China Mw6.6 (Cui et al. 2014; Chang et al. 2016), the 2010 Yushu, China Mw 6.9 (Yokota et al. 2012; Xu et al. 2014), Port-au-Prince, Haiti Mw 7.0 (John et al. 2015; Kasia 2018), the 2008 Wenchuan, China Mw7.9 (Gorum et al. 2011; Chang et al. 2017), 2002 Denali, Alaska Mw7.9 (Serva et al. 2016), 1999 Chi-chi, Taiwan, China Mw7.6 (Lee et al. 2013), and 1994 Northridge, America Mw6.7 quake (Budimir et al. 2015; Havenith et al. 2016). This task will continue to be a focused issue of coseismic landslides science studies in the future. Many scholars use several methods and techniques to evaluate the susceptibility of coseismic landsli
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