Skip to main content

Advertisement

Log in

Coseismic landslides triggered by the 2022 Luding Ms6.8 earthquake, China

  • Recent Landslides
  • Published:
Landslides Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Brenning A (2005) Spatial prediction models for landslide hazards: review, comparison and evaluation. Nat Hazard 5:853–862

    Article  Google Scholar 

  • Budimir M, Atkinson P, Lewis H (2014) Earthquake-and-landslide events are associated with more fatalities than earthquakes alone. Nat Hazards 72:895–914

    Article  Google Scholar 

  • Budimir M, Atkinson P, Lewis H (2015) A systematic review of landslide probability mapping using logistic regression. Landslides 12:419–436

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Guzzetti F, Reichenbach P, Ardizzone F, Cardinali M, Galli M (2006) Estimating the quality of landslide susceptibility models. Geomorphology 81:166–184

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Huang R, Fan X (2013) The landslide story. Nat Geosci 6:325–326

    Article  Google Scholar 

  • Keefer DK (1984) Landslides caused by earthquakes. Geol Soc Am Bull 95:406–421

    Article  Google Scholar 

  • Keefer DK (2000) Statistical analysis of an earthquake-induced landslide distribution—the 1989 Loma Prieta, California event. Eng Geol 58:231–249

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Mantovani F, Soeters R, Van Westen C (1996) Remote sensing techniques for landslide studies and hazard zonation in Europe. Geomorphology 15:213–225

    Article  Google Scholar 

  • Meunier P, Hovius N, Haines JA (2008) Topographic site effects and the location of earthquake induced landslides. Earth Planet Sci Lett 275:221–232

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Rodriguez JJ, Kuncheva LI, Alonso CJ (2006) Rotation forest: a new classifier ensemble method. IEEE Trans Pattern Anal Mach Intell 28:1619–1630

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Van Westen CJ, Castellanos E, Kuriakose SL (2008) Spatial data for landslide susceptibility, hazard, and vulnerability assessment: an overview. Eng Geol 102:112–131

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Woźniak M, Grana M, Corchado E (2014) A survey of multiple classifier systems as hybrid systems. Information Fusion 16:3–17

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Yin Y, Wang F, Sun P (2009) Landslide hazards triggered by the 2008 Wenchuan earthquake, Sichuan, China. Landslides 6:139–152

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

Download references

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).

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Xuanmei Fan or Xin Wang.

Ethics declarations

Conflict of interests

The authors declare no competing interests.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10346-023-02061-3

Keywords

Navigation