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    Geostatistical modeling to capture seismic-shaking patterns from earthquake-induced landslides

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    Type
    Article
    Authors
    Lombardo, Luigi
    Bakka, Haakon
    Tanyas, Hakan
    Westen, Cees
    Mai, Paul Martin cc
    Huser, Raphaël cc
    KAUST Department
    Computational Earthquake Seismology (CES) Research Group
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Earth Science and Engineering
    Earth Science and Engineering Program
    Physical Science and Engineering (PSE) Division
    Statistics
    Statistics Program
    Date
    2019-07-04
    Permanent link to this record
    http://hdl.handle.net/10754/656166
    
    Metadata
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    Abstract
    We investigate earthquake-induced landslides using a geostatistical model featuring a latent spatial effect (LSE). The LSE represents the spatially structured residuals in the data, which remain after adjusting for covariate effects. To determine whether the LSE captures the residual signal from a given trigger, we test the LSE in reproducing the pattern of seismic shaking from the distribution of seismically induced landslides, without prior knowledge of the earthquake being included in the model. We assessed the landslide intensity, i.e., the expected number of landslides per mapping unit, for the area in which landslides triggered by the Wenchuan and Lushan earthquakes overlap. We examined this area to test our method on landslide inventories located in near and far fields of the earthquake. We generated three models for both earthquakes: i) seismic parameters only (proxy for the trigger); ii}) the LSE only; and iii) both seismic parameters and the LSE. The three configurations share the same morphometric covariates. This allowed us to study the LSE pattern and assess whether it approximated the seismic effects. Our results show that the LSE reproduced the shaking patterns for both earthquakes. In addition, the models including the LSE perform better than conventional models featuring seismic parameters only. Due to computational limitations we carried out a detailed analysis for a relatively small area (2112 km2), using a dataset with higher spatial resolution. Results were consistent with those of a subsequent analysis for a larger area (14648 km2) using coarser resolution data.
    Citation
    Lombardo, L., Bakka, H., Tanyas, H., Westen, C., Mai, P. M., & Huser, R. (2019). Geostatistical Modeling to Capture Seismic-Shaking Patterns From Earthquake-Induced Landslides. Journal of Geophysical Research: Earth Surface, 124(7), 1958–1980. doi:10.1029/2019jf005056
    Sponsors
    We thank Xu et al.(2014), DOI: https://doi.org/10.1007/s10346-013-0404-6, and Xu et al.(2015), DOI: https://doi.org/10.1016/j.geomorph.2015.07.002, for making their landslide inventories available. Also, we would like to thank the authors of Schmitt et al. (2017), DOI: https://doi.org/10.3133/ds1064, and Tanyas et al.(2017), DOI: https://doi.org/10.1002/2017JF004236, for their effort in collating and servicing the first open repository of global earthquake-induced landslides. The two ShakeMaps are available at the following links: https://earthquake.usgs.gov/earthquakes/eventpage/usp000g650#shakemap for Wenchuan and https://earthquake.usgs.gov/earthquakes/eventpage/usb000gcdd#shakemap for Lushan.
    Publisher
    American Geophysical Union (AGU)
    Journal
    Journal of Geophysical Research: Earth Surface
    DOI
    10.1029/2019jf005056
    Additional Links
    https://onlinelibrary.wiley.com/doi/abs/10.1029/2019JF005056
    ae974a485f413a2113503eed53cd6c53
    10.1029/2019jf005056
    Scopus Count
    Collections
    Articles; Physical Science and Engineering (PSE) Division; Earth Science and Engineering Program; Statistics Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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