Earthquake-reactivated landslide scenarios in Southern Italy based on spectral-matching input analysis

n/

Semi-empirical relationships to assess the seismic performance of slopes from an updated version of the Italian seismic database

Funder: Dipartimento della Protezione Civile, Presidenza del Consiglio dei Ministri; doi: http://dx.doi.org/10.13039/100012783; Grant(s): ReLUIS research project - Working Pachage 16: Geotechnical Engineering - Task Group 2: Slope stabilityAbstractSeismic performance of slopes can be assessed through displacement-based procedures where earthquake-induced displacements are usually computed following Newmark-type calculations. These can be adopted to perform a parametric integration of earthquake records to evaluate permanent displacements for different slope characteristics and seismic input properties. Several semi-empirical relationships can be obtained for different purposes: obtaining site-specific displacement hazard curves following a fully-probabilistic approach, to assess the seismic risk associated with the slope; providing semi-empirical models within a deterministic framework, where the seismic-induced permanent displacement is compared with threshold values related to different levels of seismic performance; calibrating the seismic coefficient to be used in pseudo-static calculations, where a safety factor against limit conditions is computed. In this paper, semi-empirical relationships are obtained as a result of a parametric integration of an updated version of the Italian strong-motion database, that, in turn, is described and compared to older versions of the database and to well-known ground motion prediction equations. Permanent displacement is expressed as a function of either ground motion parameters, for a given yield seismic coefficient of the slope, or of both ground motion parameters and the seismic coefficient. The first are meant to be used as a tool to develop site-specific displacement hazard curves, while the last can be used to evaluate earthquake-induced slope displacements, as well as to calibrate the seismic coefficient to be used in a pseudo-static analysis. Influence of the vertical component of seismic motion on these semi-empirical relationships is also assessed.</jats:p

Control of style-of-faulting on spatial pattern of earthquake-triggered landslides

Predictive mapping of susceptibility to earthquake-triggered landslides (ETLs) commonly uses distance to fault as spatial predictor, regardless of style-of-faulting. Here, we examined the hypothesis that the spatial pattern of ETLs is influenced by style-of-faulting based on distance distribution analysis and Fry analysis. The Yingxiu–Beichuan fault (YBF) in China and a huge number of landslides that ruptured and occurred, respectively, during the 2008 Wenchuan earthquake permitted this study because the style-of-faulting along the YBF varied from its southern to northern parts (i.e. mainly thrust-slip in the southern part, oblique-slip in the central part and mainly strike-slip in the northern part). On the YBF hanging-wall, ETLs at 4.4–4.7 and 10.3–11.5 km from the YBF are likely associated with strike- and thrust-slips, respectively. On the southern and central parts of the hanging-wall, ETLs at 7.5–8 km from the YBF are likely associated with oblique-slips. These findings indicate that the spatial pattern of ETLs is influenced by style-of-faulting. Based on knowledge about the style-of-faulting and by using evidential belief functions to create a predictor map based on proximity to faults, we obtained higher landslide prediction accuracy than by using unclassified faults. When distance from unclassified parts of the YBF is used as predictor, the prediction accuracy is 80%; when distance from parts of the YBF, classified according to style-of-faulting, is used as predictor, the prediction accuracy is 93%. Therefore, mapping and classification of faults and proper spatial representation of fault control on occurrence of ETLs are important in predictive mapping of susceptibility to ETLs

Landslides Triggered by the MW 7.8 14 November 2016 Kaikoura Earthquake, New Zealand

The MW 7.8 14 November 2016 Kaikoura earthquake generated more than 10000 landslides over a total area of about 10000 km2, with the majority concentrated in a smaller area of about 3600 km2. The largest landslide triggered by the earthquake had an approximate volume of 20 (±2) M m3, with a runout distance of about 2.7 km, forming a dam on the Hapuku River. In this paper, we present version 1.0 of the landslide inventory we have created for this event. We use the inventory presented in this paper to identify and discuss some of the controls on the spatial distribution of landslides triggered by the Kaikoura earthquake. Our main findings are (1) the number of medium to large landslides (source area ≥10000 m2) triggered by the Kaikoura earthquake is smaller than for similar sized landslides triggered by similar magnitude earthquakes in New Zealand; (2) seven of the largest eight landslides (from 5 to 20 x 106 m3) occurred on faults that ruptured to the surface during the earthquake; (3) the average landslide density within 200 m of a mapped surface fault rupture is three times that at a distance of 2500 m or more from a mapped surface fault rupture ; (4) the “distance to fault” predictor variable, when used as a proxy for ground-motion intensity, and when combined with slope angle, geology and elevation variables, has more power in predicting landslide probability than the modelled peak ground acceleration or peak ground velocity; and (5) for the same slope angles, the coastal slopes have landslide point densities that are an order of magnitude greater than those in similar materials on the inland slopes, but their source areas are significantly smaller

A seismic landslide susceptibility rating of geologic units based on analysis of characterstics of landslides triggered by the 17 January, 1994 Northridge, California earthquake

One of the most significant effects of the 17 January, 1994 Northridge, California earthquake (M=6.7) was the triggering of thousands of landslides over a broad area. Some of these landslides damaged and destroyed homes and other tructures, blocked roads, disrupted pipelines, and caused other serious damage. Analysis of the distribution and characteristics of these landslides is important in understanding what areas may be susceptible to landsliding in future earthquakes. We analyzed the frequency, distribution, and geometries of triggered landslides in the Santa Susana 7.5′ quadrangle, an area of intense seismic landslide activity near the earthquake epicenter. Landslides occured primarily in young (Late Miocene through Pleistocene) uncemented or very weakly cemented sediment that has been repeatedly folded, faulted, and uplifted in the past 1.5 million years. The most common types of landslide triggered by the earthquake were highly disrupted, shallow falls and slides of rock and debris. Far less numerous were deeper, more coherent slumps and block slides, primarily occuring in more cohesive or competent materials. The landslides in the Santa Susana quadrangle were divided into two samples: single landslides (1502) and landslide complexes (60), which involved multiple coalescing failures of surficial material. We described landslide, morphologies by computing simple morphometric parameters (area, length, width, aspect ratio, slope angle). To quantify and rank the relative susceptibility of each geologic unit to seismic landsliding, we calculated two indices: (1) the susceptibility index, which is the ratio (given as a percentage) of the area covered by landslide sources within a geologic unit to the total outcrop area of that unit: and (2) the frequency index [given in landslides per square kilometer (ls/km2)], which is the total number of landslides within each geologic unit divided by the outcrop area of that unit. Susceptibility categories include very high (&gt;2.5% landslide area or &gt;30 1s/km2). high (1.0-2.5% landslide area or 10-30 1s/km2), moderate (0.5-1.0% landslide area or 3-10 1s/km2), and low (&lt;0.5% landslide area and &lt;3 1s/km2). © 2000 Elsevier Science B.V. All rights reserved