From scenario-based seismic hazard to scenario-based landslide hazard: rewinding to the past via statistical simulations

Abstract

The vast majority of statistically-based landslide susceptibility studies assumes the slope instability process to be time-invariant under the definition that “the past and present are keys to the future”. This assumption may generally be valid. However, the trigger, be it a rainfall or an earthquake event, clearly varies over time. And yet, the temporal component of the trigger is rarely included in landslide susceptibility studies and only confined to hazard assessment. In this work, we investigate a population of landslides triggered in response to the 2017 Jiuzhaigou earthquake ($$M_w = 6.5$$ M w = 6.5 ) including the associated ground motion in the analyses, these being carried out at the Slope Unit (SU) level. We do this by implementing a Bayesian version of a Generalized Additive Model and assuming that the slope instability across the SUs in the study area behaves according to a Bernoulli probability distribution. This procedure would generally produce a susceptibility map reflecting the spatial pattern of the specific trigger and therefore of limited use for land use planning. However, we implement this first analytical step to reliably estimate the ground motion effect, and its distribution, on unstable SUs. We then assume the effect of the ground motion to be time-invariant, enabling statistical simulations for any ground motion scenario that occurred in the area from 1933 to 2017. As a result, we obtain the full spectrum of potential coseismic susceptibility patterns over the last century and compress this information into a hazard model/map representative of all the possible ground motion patterns since 1933. This backward statistical simulations can also be further exploited in the opposite direction where, by accounting for scenario-based ground motion, one can also use it in a forward direction to estimate future unstable slopes.

Landslide susceptibility maps of Italy: lesson learnt from dealing with multiple landslide classes and the uneven spatial distribution of the national inventory

“…Das et al (2012) show one example of Bayesian GLM to assess the landslide susceptibility in the proximity of roads in a Indian case study. Analogous examples can be found more recently in catchment Luo et al, 2021) and and regional scale assessments (Tanyaş et al, 2021). Recently, Lombardo et al (2018aLombardo et al ( , 2019 proposed an extension of the Bayesian workflow pursued by the authors mentioned above by using a Log-Gaussian Point Process to predict landslide counts per mapping unit, this being implemented in R-INLA (Lindgren and Rue, 2015;Bakka et al, 2018).…”

Multi-Temporal Landslide Inventory-Based Statistical Susceptibility Modeling Associated With the 2017 Mw 6.5 Jiuzhaigou Earthquake, Sichuan, China

“…All of these active structures have the potential for triggering earthquakes of magnitude M L ≥ 7.5 (Li et al, 2016). According to historical earthquake records, this region was affected by seven strong earthquake events in the last century, including the 1933 Diexi M w 7.3 earthquake, 1960 Songpan M w 6.3 earthquake, 1973 Songpan M w 6.1 earthquake, 1974 Songpan M w 5.7 earthquake, and 1976 Songpan-Pingwu earthquake swarm (M w 6.9 occurred on 16 August, M w 6.4 on 21 August, and M w 6.7 on 23 August) (see Figure 2 in Luo et al, 2021). These events may have caused serious damage to the rock masses, increasing the probability of slope failure.…”

Performance Assessment of Event-Based Ensemble Landslide Susceptibility Models in Shihmen Watershed, Taiwan

“…Furthermore, when establishing a nonparametric machine learning model, the training set is also used to perform hyperparameter optimization. During the optimization process, fivefold cross-validation [9] and tenfold cross-validation [12,30] are often used to tune the hyperparameters.…”