Abstract
On September 5, 2022, an Ms6.8 earthquake struck Luding County, Sichuan Province, China. Through creating a coseismic landslide prediction model, we obtained the spatial distribution of the triggered geological hazards immediately after the earthquake. Through collecting all available multi-source optical remote sensing images of the earthquake-affected area via UAV and satellite platforms, the exact information of coseismic landslide was achieved by pattern recognition and visual inspection. According to the current results, the Luding earthquake triggered 5336 landslides with a total area of 28.53km2. The spatial distribution of the coseismic landslides is correlated statistically to various seismic, terrain, and geological factors, to evaluate their susceptibility at regional scale and to identify the most typical characteristics of these failures. The results reveal that the coseismic landslides mainly occurred on the sides of the Xianshuihe fault (within 1.2 km) and Dadu River (within 0.5 km) in striped patterns. They are concentrated in the regions with an elevation range of 1000–1800 m, a slope range of 25–55°, and lithologies of acid plutonic rocks, mixed sedimentary rocks, and siliciclastic sedimentary rocks. Besides, the coseismic landslides of the Luding earthquake are smaller in size and shallower than those triggered by the 2008 Wenchuan earthquake and the 2017 Jiuzhaigou earthquake. Rapidly achieving the spatial locations and distribution patterns of the coseismic landslides enables to provide effective support and guidance to emergency rescue, risk mitigation, and reconstruction planning.
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References
An Y, Wang D, Ma Q, Xu Y, Li Y, Zhang Y, Liu Z, Huang C, Su J, Li J, Li M, Chen W, Wan Z, Kang D, Wang B (2022) Preliminary report of the 5 September 2022 MS 6.8 Luding earthquake, Sichuan, China. Earthq Res Adv. https://doi.org/10.1016/j.eqrea.2022.100184
Bai M, Chevalier ML, Leloup PH, Li H, Pan J, Replumaz A, Wang S, Li K, Wu Q, Liu F (2021) Spatial slip rate distribution along the SE Xianshuihe fault, eastern Tibet, and earthquake hazard assessment. Tectonics 40:e2021TC006985
Bai M, Chevalier M-L, Pan J, Replumaz A, Leloup PH, Métois M, Li H (2018) Southeastward increase of the late Quaternary slip-rate of the Xianshuihe fault, eastern Tibet. Geodynamic and seismic hazard implications. Earth Planet Sci Lett 485:19–31
Bao H, Ampuero J-P, Meng L, Fielding EJ, Liang C, Milliner CW, Feng T, Huang H (2019) Early and persistent supershear rupture of the 2018 magnitude 7.5 Palu earthquake. Nat Geosci 12:200–205
Belgiu M, Drăguţ L (2016) Random forest in remote sensing: a review of applications and future directions. ISPRS J Photogramm Remote Sens 114:24–31
Benediktsson JA, Palmason JA, Sveinsson JR (2005) Classification of hyperspectral data from urban areas based on extended morphological profiles. IEEE Trans Geosci Remote Sens 43:480–491
Brenning A (2005) Spatial prediction models for landslide hazards: review, comparison and evaluation. Nat Hazard 5:853–862
Budimir M, Atkinson P, Lewis H (2014) Earthquake-and-landslide events are associated with more fatalities than earthquakes alone. Nat Hazards 72:895–914
Budimir M, Atkinson P, Lewis H (2015) A systematic review of landslide probability mapping using logistic regression. Landslides 12:419–436
Chen G, Xu X, Wen X, Chen YG (2016) Late Quaternary slip-rates and slip partitioning on the southeastern Xianshuihe fault system, eastern Tibetan Plateau. Acta Geologica Sinica-English Edition 90:537–554
Crippen R, Buckley S, Belz E, Gurrola E, Hensley S, Kobrick M, Lavalle M, Martin J, Neumann M, Nguyen Q (2016) NASADEM global elevation model: methods and progress. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 41:125–128
Dalla Mura M, Benediktsson JA, Chanussot J, Bruzzone L (2011) The evolution of the morphological profile: from panchromatic to hyperspectral images. Optical Remote Sens 123–146. Springer
Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S (2020) An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929
Fang S (2023) Prediction-model-of-coseismic-landslides: https://github.com/fshutong/Prediction-model-of-coseismiclandslides.git. Accessed 31 Mar 2023
Fan X, Fang C, Dai L, Wang X, Luo Y, Wei T, Wang Y (2022) Near real time prediction of spatial distribution probability of earthquake-induced landslides-Take the Lushan Earthquake on June 1, 2022 as an example. J Eng Geol 30:729–739. https://doi.org/10.13544/j.cnki.jeg.2022-0328
Fan X, Scaringi G, Korup O, West AJ, van Westen CJ, Tanyas H, Hovius N, Hales TC, Jibson RW, Allstadt KE (2019) Earthquake-induced chains of geologic hazards: patterns, mechanisms, and impacts. Rev Geophys 57:421–503
Fan X, Scaringi G, Xu Q, Zhan W, Dai L, Li Y, Pei X, Yang Q, Huang R (2018) Coseismic landslides triggered by the 8th August 2017 Ms 7.0 Jiuzhaigou earthquake (Sichuan, China): factors controlling their spatial distribution and implications for the seismogenic blind fault identification. Landslides 15:967–983
Fan X, Yunus AP, Scaringi G, Catani F, Siva Subramanian S, Xu Q, Huang R (2021) Rapidly evolving controls of landslides after a strong earthquake and implications for hazard assessments. Geophys Res Lett 48:e2020GL090509
Galli M, Ardizzone F, Cardinali M, Guzzetti F, Reichenbach P (2008) Comparing landslide inventory maps. Geomorphology 94:268–289
Gorum T, Korup O, van Westen CJ, van der Meijde M, Xu C, van der Meer FD (2014) Why so few? Landslides triggered by the 2002 Denali earthquake, Alaska. Quatern Sci Rev 95:80–94
Guzzetti F, Mondini AC, Cardinali M, Fiorucci F, Santangelo M, Chang K-T (2012) Landslide inventory maps: new tools for an old problem. Earth Sci Rev 112:42–66
Guzzetti F, Reichenbach P, Ardizzone F, Cardinali M, Galli M (2006) Estimating the quality of landslide susceptibility models. Geomorphology 81:166–184
Hartmann J, Moosdorf N (2012) The new global lithological map database GLiM: a representation of rock properties at the Earth surface. Geochem Geophys Geosyst 13
Hovius N, Stark CP, Allen PA (1997) Sediment flux from a mountain belt derived by landslide mapping. Geology 25:231–234
Huang R, Fan X (2013) The landslide story. Nat Geosci 6:325–326
Keefer DK (1984) Landslides caused by earthquakes. Geol Soc Am Bull 95:406–421
Keefer DK (2000) Statistical analysis of an earthquake-induced landslide distribution—the 1989 Loma Prieta, California event. Eng Geol 58:231–249
Kincey ME, Rosser NJ, Robinson TR, Densmore AL, Shrestha R, Pujara DS, Oven KJ, Williams JG, Swirad ZM (2021) Evolution of coseismic and post‐seismic landsliding after the 2015 Mw 7.8 Gorkha earthquake, Nepal. J Geophys Res: Earth Surf 126:e2020JF005803
Lee C-T, Huang C-C, Lee J-F, Pan K-L, Lin M-L, Dong J-J (2008) Statistical approach to earthquake-induced landslide susceptibility. Eng Geol 100:43–58
Lee S (2005) Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data. Int J Remote Sens 26:1477–1491
Li G, West AJ, Densmore AL, Hammond DE, Jin Z, Zhang F, Wang J, Hilton RG (2016) Connectivity of earthquake-triggered landslides with the fluvial network: implications for landslide sediment transport after the 2008 Wenchuan earthquake. J Geophys Res Earth Surf 121:703–724
Liu Y, Chu L, Chen G et al (2021) Paddleseg: a high-efficient development toolkit for image segmentation. arXiv preprint arXiv:2101.06175
Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101
Lyu M, Xie J, Ukonmaanaho L, Jiang M, Li Y, Chen Y, Yang Z, Zhou Y, Lin W, Yang Y (2017) Land use change exerts a strong impact on deep soil C stabilization in subtropical forests. J Soils Sediments 17(9):2305–2317
Mantovani F, Soeters R, Van Westen C (1996) Remote sensing techniques for landslide studies and hazard zonation in Europe. Geomorphology 15:213–225
Meunier P, Hovius N, Haines JA (2008) Topographic site effects and the location of earthquake induced landslides. Earth Planet Sci Lett 275:221–232
Ministry of Emergency Management releases intensity map of Luding magnitude 6.8 earthquake in Sichuan Province - Ministry of Emergency Management, PRC. www.mem.gov.cn/xw/yjglbgzdt/202209/t20220911_422190.shtml. Accessed 31 Mar 2023.
Mountrakis G, Im J, Ogole C (2011) Support vector machines in remote sensing: a review. ISPRS J Photogramm Remote Sens 66:247–259
Nowicki Jessee M, Hamburger M, Allstadt K, Wald DJ, Robeson S, Tanyas H, Hearne M, Thompson E (2018) A global empirical model for near-real-time assessment of seismically induced landslides. J Geophys Res Earth Surf 123:1835–1859
Reichenbach P, Rossi M, Malamud BD, Mihir M, Guzzetti F (2018) A review of statistically-based landslide susceptibility models. Earth Sci Rev 180:60–91
Rodriguez JJ, Kuncheva LI, Alonso CJ (2006) Rotation forest: a new classifier ensemble method. IEEE Trans Pattern Anal Mach Intell 28:1619–1630
Rossi G, Tanteri L, Tofani V, Vannocci P, Moretti S, Casagli N (2018) Multitemporal UAV surveys for landslide mapping and characterization. Landslides 15:1045–1052
Roy DP, Wulder MA, Loveland TR, Woodcock CE, Allen RG, Anderson MC, Helder D, Irons JR, Johnson DM, Kennedy R (2014) Landsat-8: science and product vision for terrestrial global change research. Remote Sens Environ 145:154–172
Tang C, Zhu J, Qi X, Ding J (2011) Landslides induced by the Wenchuan earthquake and the subsequent strong rainfall event: a case study in the Beichuan area of China. Eng Geol 122:22–33
Tang X, Tu Z, Wang Y, Liu M, Li D, Fan X (2022) Automatic detection of coseismic landslides using a new transformer method. Remote Sens 14:2884
Tanyas H, Rossi M, Alvioli M, van Westen CJ, Marchesini I (2019) A global slope unit-based method for the near real-time prediction of earthquake-induced landslides. Geomorphology 327:126–146
Valagussa A, Marc O, Frattini P, Crosta G (2019) Seismic and geological controls on earthquake-induced landslide size. Earth Planet Sci Lett 506:268–281
Van Westen CJ, Castellanos E, Kuriakose SL (2008) Spatial data for landslide susceptibility, hazard, and vulnerability assessment: an overview. Eng Geol 102:112–131
Wang F, Fan X, Yunus AP, Siva Subramanian S, Alonso-Rodriguez A, Dai L, Xu Q, Huang R (2019) Coseismic landslides triggered by the 2018 Hokkaido, Japan (Mw 6.6), earthquake: spatial distribution, controlling factors, and possible failure mechanism. Landslides 16:1551–1566
Wang X, Fan X, Xu Q, Du P (2022) Change detection-based co-seismic landslide mapping through extended morphological profiles and ensemble strategy. ISPRS J Photogramm Remote Sens 187:225–239
Williams JG, Rosser NJ, Hardy RJ, Brain MJ, Afana AA (2018) Optimising 4-D surface change detection: an approach for capturing rockfall magnitude–frequency. Earth Surf Dyn 6:101–119
Woźniak M, Grana M, Corchado E (2014) A survey of multiple classifier systems as hybrid systems. Information Fusion 16:3–17
Xie E, Wang W, Yu Z, Anandkumar A, Alvarez JM, Luo P (2021) SegFormer: simple and efficient design for semantic segmentation with transformers. Adv Neural Inf Process Syst 34:12077–12090
Xu C, Xu X, Yao X, Dai F (2014) Three (nearly) complete inventories of landslides triggered by the May 12, 2008 Wenchuan Mw 7.9 earthquake of China and their spatial distribution statistical analysis. Landslides 11:441–461
Xu Q, Zhang S, Li W, Van Asch TW (2012) The 13 August 2010 catastrophic debris flows after the 2008 Wenchuan earthquake, China. Nat Hazard 12:201–216
Yin Y, Wang F, Sun P (2009) Landslide hazards triggered by the 2008 Wenchuan earthquake, Sichuan, China. Landslides 6:139–152
Zhao B, Li W, Su L, Wang Y, Wu H (2022) Insights into the landslides triggered by the 2022 Lushan Ms 6.1 earthquake: spatial distribution and controls. Remote Sens 14:4365
Zhou Z-H, Feng J (2019) Deep Forest National Science Review 6:74–86
Acknowledgements
We thank the Sichuan Bureau of Surveying, Mapping and Geographic Information, Chengdu Jouav Automation Tech Co., Ltd., and Wuhan Dida Information Engineering Co., LTD., for providing satellite- and UAV-based remote sensing images. We also thank the Sichuan Earthquake Administration for providing the seismic intensity and PGA maps.
Funding
This research is financially supported by the National Science Fund for Distinguished Young Scholars of China (Grant No.42125702), the Tencent Foundation through the XPLORER PRIZE (Grant No.XPLORER-2022-1012), and the Natural Science Foundation of Sichuan Province (Grant No. 2022NSFSC0003).
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Dai, L., Fan, X., Wang, X. et al. Coseismic landslides triggered by the 2022 Luding Ms6.8 earthquake, China. Landslides 20, 1277–1292 (2023). https://doi.org/10.1007/s10346-023-02061-3
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DOI: https://doi.org/10.1007/s10346-023-02061-3