Evaluation of Seismic Landslide Susceptibility by Integrating Statistical Learning Model and Newmark Model—A Case Study of the Wenchuan Earthquake

Whether it can quickly and effectively predict the susceptibility of regional earthquake landslides to achieve rapid rescue, loss assessment and post-disaster reconstruction has always been a difficult problem. However, the traditional high-precision evaluation of seismic landslide susceptibility often relies heavily on the complete or incomplete landslide inventory, which is poor in timeliness and cannot effectively evaluate the target area before or shortly after the earthquake. In most cases, the Newmark model relies on experts’ experience to select model parameters, therefore the evaluation result of this method is unstable and it lacks strong generalization ability. A fused model is proposed to classify the positive and negative training samples of the study area through the evaluation results of the Newmark model under the slope units, and it applies a variety of statistical learning models to evaluate the landslide susceptibility of the Wenchuan earthquake based on the classification results of the Newmark model. The results show that the evaluation of the statistical learning model fused with the Newmark model has higher accuracy. This method can overcome the inherent shortcomings of a single Newmark model to obtain better evaluation results without relying on obtaining the complete landslide inventory. Meanwhile, the model can be applied to quickly obtain the evaluation results of regional landslide susceptibility before or shortly after the earthquake, thereby effectively reducing human and economic losses caused by earthquake landslides.

Seismic Landslide Susceptibility Assessment Using Principal Component Analysis And Support Vector Machine

Seismic landslides are dangerous natural hazards, causing immense damage in terms of human lives and property. Susceptibility assessment of earthquake triggered landslide is the scientific premise and theoretical basis of disaster emergency management of engineering. The aim of this study is to applied the seismic landslide susceptibility model to Dayong Expressway in Chenghai area prone to frequent earthquakes. Support vector machine is used to establish the assessment model based on the data of 716 landslides caused by Ludian Ms6.5 earthquake in 2014. To improve the universality of the assessment model in different regions. Principal component analysis (PCA) is used for reducing the dimension of landslide conditioning factors and weaking difference of the regional characteristics between historical earthquake regions with Dayong expressway area. To applied the SVM model for seismic landslide susceptibility in Dayong Expressway region where the conditioning factors information is similar to Ludian area. Gutenberg-Richter model and Dieterich model are used to assume an earthquake in Chenghai area for landslide susceptibility assessment. Inverse distance weight (IDW) method is used for assessing the landslide risk class of Dayong Expressway. The results show that the “Very high” landslide susceptibility class account for 0.63% of Chenghai area. The seismic landslide has the most obvious impact on the middle 13 km section of Dayong expressway and this section account for 8.9% is defined as high-risk class. The study verifies the practicability of the seismic landslide susceptibility model based on machine learning and provides constructive reference for the susceptibility assessment of engineering facilities under earthquake.

Discovering Vegetation Recovery and Landslide Activities in the Wenchuan Earthquake Area with Landsat Imagery

Post-seismic vegetation recovery is critical to local ecosystem recovery and slope stability, especially in the Wenchuan earthquake area where tens of thousands of landslides were triggered. This study executed a decadal monitoring of post-seismic landslide activities all over the region by investigating landslide vegetation recovery rate (VRR) with Landsat images and a (nearly) complete landslide inventory. Thirty thousand landslides that were larger than nine pixels were chosen for VRR analysis, to reduce the influence of mixed pixels and support detailed investigation within landslides. The study indicates that about 60% of landslide vegetation gets close to the pre-earthquake level in ten years and is expected to recover to the pre-earthquake level within 20 years. The vegetation recovery is significantly influenced by topographic factors, especially elevation and slope, while it is barely related to the distance to epicenter, fault ruptures, and rivers. This study checked and improved the knowledge of vegetation recovery and landslide stability in the area, based on a detailed investigation.

Ambient noise and ERT data provide insights into the structure of co-seismic rock avalanche deposits in Sichuan (China)

The post-seismic history of the 2008 Mw7.9 Wenchuan earthquake shows that marginally stable deposits of large co-seismic landslide dams can pose persistent debris flow hazards for the downstream areas. Here, we combine analyses of single-station recordings of ambient noise with electrical resistivity tomography (ERT) surveys to explore the potential of drawing information on structure and geometry of the deposit of a large rock avalanche triggered by the Mw 7.9 2008 Wenchuan earthquake, which dammed the Yangjia stream in the Sichuan Province (China). The substantial thickness and heterogeneity of this kind of deposits limit the application of standard geophysical techniques, like active seismic surveys, which require highly energetic sources and long linear geophone arrays to reach adequate investigation depths. Passive single-station methods, relying on ambient noise recordings to determine site resonance properties, controlled by the contrast between soft surface layers and a stiffer substratum, offer the opportunity of investigating subsoil properties down to larger depths. In particular, we use a recently developed technique, which isolates the contribution of Rayleigh waves to ambient noise and draws information on sub-soil properties from the inversion of Rayleigh wave ellipticity curves plotted as function of frequency. In this framework, the ERT data can support the ellipticity curve inversion, typically affected by highly non-univocal solutions, by providing constraints for defining of the thickness of the uppermost surficial layers. The results allowed inferring the overlap of different layers within the 2008 rock avalanche deposit, as well as estimating lateral variations in their thickness and S-wave (Vs) velocities.

New Insight into Post-seismic Landslide Evolution Processes in the Tropics

Earthquakes do not only trigger landslides in co-seismic phases but also elevate post-seismic landslide susceptibility either by causing a strength reduction in hillslope materials or by producing co-seismic landslide deposits, which are prone to further remobilization under the external forces generated by subsequent rainfall events. However, we still have limited observations regarding the post-seismic landslide processes. And, the examined cases are rarely representative of tropical conditions where the precipitation regime is strong and persistent. Therefore, in this study, we introduce three new sets of multi-temporal landslide inventories associated with subsets of the areas affected by 1) 2016 Reuleuet (Indonesia, Mw = 6.5), 2) 2018 Porgera (Papua New Guinea, Mw = 7.5) and 3) 2012 Sulawesi (Indonesia, Mw = 6.3), 2017 Kasiguncu (Indonesia, Mw = 6.6) and 2018 Palu (Indonesia, Mw = 7.5) earthquakes. Overall, our findings show that the landslide susceptibility level associated with the occurrences of new landslides return to pre-seismic conditions in less than a year in the study areas under consideration. We stress that these observations might not be representative of the entire area affected by these earthquakes but the areal boundaries of our study areas.

Probabilistic assessment of seismic-induced slope displacements: an application in Italy

Earthquake-induced slope instability is one of the most important hazards related to ground shaking, causing damages to the environment and, often, casualties. Therefore, it is important to assess the seismic performance of slopes, especially in the near fault regions, evaluating the permanent displacements induced by seismic loading. This paper applies a probabilistic approach to evaluate the seismic performance of slopes using an updated database of ground motions recorded during the earthquakes occurred in Italy. The main advantage of this approach is that of accounting for the aleatory variability of both ground motions and prediction of seismic-induced displacements of slopes. The results are presented in terms of hazard curves, showing the annual rate of exceedance of permanent slope displacement evaluated using ground motion data provided by a standard probabilistic hazard analysis and a series of semi-empirical relationships linking the permanent displacements of slopes to one or more ground motion parameters. The procedure has been implemented on a regional scale to produce seismic landslide hazard maps for the Irpinia district, in Southern Italy, characterised by a severe seismic hazard. Seismic landslide hazard maps represent a useful tool for practitioners and government agencies for a regional planning to identify and monitor zones that are potentially susceptible to earthquake-induced slope instability, thus requiring further detailed, site-specific studies.