Geostatistical Modeling to Capture Seismic‐Shaking Patterns From Earthquake‐Induced Landslides
By Luigi Lombardo, H. Bakka, H. Tanyaș, Cees J. van Westen, P. Martin Mai, Raphaël Huser. Journal of Geophysical Research - Earth Surface, 2018
74 citations recorded by the source corpus.
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, that is, 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 (2,112 km2), using a data set with higher spatial resolution. Results were consistent with those of a subsequent analysis for a larger area (14,648 km2) using coarser‐resolution data.
Subfields: Aerospace Engineering, Geophysics, Management, Monitoring, Policy and Law
View Paper: https://www.semanticscholar.org/paper/50bfde9858d57b8a2bff33c06bf27bf7c4c65160
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In Management, Monitoring, Policy and Law
Citations in the corpus
- Accounting for covariate distributions in slope-unit-based landslide susceptibility models. A case study in the alpine environment (2019, background): “One possible interpretation is that morphometric variables and SU characteris-531 tics can spatially carry some information coincident with lithotechnical parameters leading532 to landslide presence or even clustering effects (Lombardo et al., 2019).”