The Evaluation of Lithology Effects on Wenchuan Seismic Landslides

2012 ◽  
Vol 166-169 ◽  
pp. 2483-2489
Author(s):  
Meng Han ◽  
Xiao Yi Fan
Keyword(s):  
Combined Effects ◽  
Contribution Rate ◽  
Landslide Area ◽  
Height Difference ◽  
Seismic Landslides ◽  
Landslide Volume ◽  
Gravelly Soil ◽  
Property Losses ◽  
Seismic Landslide ◽  
Landslide Development

The landslides caused serious casualties and property losses in Wenchuan earthquake, China. According to the survey data of the landslides, the landslide lithology, contribution rate of lithology, height difference and volume of the landslides were studied. The results indicated that the 13 lithologies had the larger relationship with seismic landslide development. In these lithologies, The Phyllite (Ph) had the greatest contribution rate on the landslide number, landslide area and landslide volume. The Stone soil (Ss) had the largest volume of landslide on unit landslide area. Metamorphic sandstone (Ms), Gravelly soil (Gs), Sandstone (Sa), Phyllite (Ph), Limestone (Li) had the greater combined effects on the landslide development.

scholarly journals Landslides triggered by the 12 January 2010 Mw 7.0 Port-au-Prince, Haiti, earthquake: visual interpretation, inventory compiling and spatial distribution statistical analysis

2014 ◽  
Vol 2 (2) ◽  
pp. 1259-1331 ◽  
Author(s):  
C. Xu ◽  
J. B. H. Shyu ◽  
X.-W. Xu
Keyword(s):  
Spatial Distribution ◽  
Slope Angle ◽  
Slope Aspect ◽  
Haiti Earthquake ◽  
Number Density ◽  
Landslide Area ◽  
Seismic Landslides ◽  
Seismic Landslide ◽  
Area Percentage ◽  
Landslide Area Percentage

Abstract. The 12 January 2010 Port-au-Prince, Haiti, earthquake (Mw 7.0) triggered tens of thousands of landslides. The purpose of this study is to investigate the correlations of the occurrence of landslides and their erosion thicknesses with topographic factors, seismic parameters, and their distance from roads. A total of 30 828 landslides triggered by the earthquake covered a total area of 15.736 km2, distributed in an area more than 3000 km2, and the volume of landslide accumulation materials is estimated to be about 29 700 000 m3. These landslides are of various types, mostly belonging to shallow disrupted landslides and rock falls, but also include coherent deep-seated landslides and rock slides. These landslides were delineated using pre- and post-earthquake high-resolutions satellite images. Spatial distribution maps and contour maps of landslide number density, landslide area percentage, and landslide erosion thickness were constructed in order to analyze the spatial distribution patterns of co-seismic landslides. Statistics of size distribution and morphometric parameters of co-seismic landslides were carried out and were compared with other earthquake events in the world. Four proxies of co-seismic landslide abundance, including landslides centroid number density (LCND), landslide top number density (LTND), landslide area percentage (LAP), and landslide erosion thickness (LET) were used to correlate co-seismic landslides with various landslide controlling parameters. These controlling parameters include elevation, slope angle, slope aspect, slope curvature, topographic position, distance from drainages, lithology, distance from the epicenter, distance from the Enriquillo–Plantain Garden fault, distance along the fault, and peak ground acceleration (PGA). A comparison of these impact parameters on co-seismic landslides shows that slope angle is the strongest impact parameter on co-seismic landslide occurrence. Our co-seismic landslide inventory is much more detailed than other inventories in several previous publications. Therefore, we carried out comparisons of inventories of landslides triggered by the Haiti earthquake with other published results and proposed possible reasons of any differences. We suggest that the empirical functions between earthquake magnitude and co-seismic landslides need to update on the basis of the abundant and more complete co-seismic landslide inventories recently available.

scholarly journals Landslides triggered by the 12 January 2010 Port-au-Prince, Haiti, <i>M</i><sub>w</sub> = 7.0 earthquake: visual interpretation, inventory compiling, and spatial distribution statistical analysis

2014 ◽  
Vol 14 (7) ◽  
pp. 1789-1818 ◽  
Author(s):  
C. Xu ◽  
J. B. H. Shyu ◽  
X. Xu
Keyword(s):  
Spatial Distribution ◽  
Slope Angle ◽  
Slope Aspect ◽  
Haiti Earthquake ◽  
Number Density ◽  
Landslide Area ◽  
Seismic Landslides ◽  
Seismic Landslide ◽  
Area Percentage ◽  
Landslide Area Percentage

Abstract. The 12 January 2010 Port-au-Prince, Haiti, earthquake (Mw= 7.0) triggered tens of thousands of landslides. The purpose of this study is to investigate the correlations of the occurrence of landslides and the thicknesses of their erosion with topographic, geologic, and seismic parameters. A total of 30 828 landslides triggered by the earthquake covered a total area of 15.736 km2, distributed in an area more than 3000 km2, and the volume of landslide accumulation materials is estimated to be about 29 700 000 m3. These landslides are of various types, mostly belonging to shallow disrupted landslides and rock falls, but also include coherent deep-seated landslides and rock slides. These landslides were delineated using pre- and post-earthquake high-resolution satellite images. Spatial distribution maps and contour maps of landslide number density, landslide area percentage, and landslide erosion thickness were constructed in order to analyze the spatial distribution patterns of co-seismic landslides. Statistics of size distribution and morphometric parameters of co-seismic landslides were carried out and were compared with other earthquake events in the world. Four proxies of co-seismic landslide abundance, including landslides centroid number density (LCND), landslide top number density (LTND), landslide area percentage (LAP), and landslide erosion thickness (LET) were used to correlate co-seismic landslides with various environmental parameters. These parameters include elevation, slope angle, slope aspect, slope curvature, topographic position, distance from drainages, lithology, distance from the epicenter, distance from the Enriquillo–Plantain Garden fault, distance along the fault, and peak ground acceleration (PGA). A comparison of these impact parameters on co-seismic landslides shows that slope angle is the strongest impact parameter on co-seismic landslide occurrence. Our co-seismic landslide inventory is much more detailed than other inventories in several previous publications. Therefore, we carried out comparisons of inventories of landslides triggered by the Haiti earthquake with other published results and proposed possible reasons for any differences. We suggest that the empirical functions between earthquake magnitude and co-seismic landslides need to be updated on the basis of the abundant and more complete co-seismic landslide inventories recently available.

scholarly journals Topographic changes due to the 2004 Chuetsu thrusting earthquake in low mountain region

2019 ◽  
Author(s):  
Zhikun Ren ◽  
Takashi Oguchi ◽  
Peizhen Zhang ◽  
Shoichiro Uchiyama
Keyword(s):  
Scaling Laws ◽  
Catchment Scale ◽  
Volume Estimate ◽  
Epicentral Area ◽  
Quantitative Studies ◽  
Landslide Volume ◽  
Seismically Induced Landslides ◽  
Seismic Landslide ◽  
Topographic Evolution

Abstract. The co-seismic landslide volume information is critical to understanding the role of strong earthquake in topographic evolution. However, the co-seismic landslide volumes are mainly obtained using statistical scaling laws, which are not accurate enough for quantitative studies of the spatial pattern of co-seismically induced erosion and the topographic changes caused by the earthquakes. The availability of both pre- and post- earthquake high-resolution DEMs provide us the opportunity to try new approach to get robust landslide volume information. Here, we propose a new method in landslide volume estimate and tested it in Chuetsu region, where a Mw 6.6 earthquake occurred in 2004. Firstly, we align the DEMs by reconstructing the horizontal difference, then we quantitatively obtained the landslide volume in the epicentral area by differencing the pre- and post-earthquake DEMs. We convert the landslide volume into the distribution of average catchment-scale seismically induced denudation. Our results indicate the preserved topography is not only due to the uplifting caused by fault-related folding on the hangwall of Muikamachi fault, but also undergone erosion caused by the seismically induced landslides. Our findings reveal that Chuetsu earthquake mainly roughens the topography in the Chuetsu region of low elevation. This study also reveal that the differential DEM method is a valuable approach in analyzing landslide volume, as well as quantitative geomorphic analysis.

scholarly journals Prediction of the area affected by earthquake-induced landsliding based on seismological parameters

2017 ◽  
Author(s):  
Odin Marc ◽  
Patrick Meunier ◽  
Niels Hovius
Keyword(s):  
Scaling Laws ◽  
Distribution Area ◽  
Rupture Directivity ◽  
Source Depth ◽  
Landslide Area ◽  
Earthquake Parameters ◽  
Critical Acceleration ◽  
Ground Shaking ◽  
Landslide Volume ◽  
Landslide Distribution

Abstract. We present an analytical, seismologically consistent expression for the surface area of the region within which landslides induced by a given earthquake are distributed. The expression is based on scaling laws relating seismic moment, source depth and focal mechanism with ground shaking and fault rupture length and assumes a globally constant critical acceleration for onset of systematic mass wasting. The seismological assumptions are identical to those recently used to propose a seismologically consistent expression for total landslide volume and area. To test the accuracy of the model we gathered geophysical information and estimates of the landslide distribution area for 83 earthquakes. To reduce uncertainties and inconsistencies in the estimation of the landslide distribution area, we propose an objective definition based on the shortest distance from the seimsic wave emission line containing 95 % of the total landslide area. Without any empirical calibration the model explains 56 % of the variance in our dataset, and predicts 35 to 49 out of 83 cases within a factor two, depending on how we account for uncertainties on the seismic source depth. For most cases with comprehensive landslide inventories we show that our prediction compares well with the smallest region around the fault containing 95 % of the total landslide area. Aspects ignored by the model that could explain the residuals include, local variations of the critical acceleration and processes modulating the surface ground shaking, such as the distribution of seismic energy release on the fault plane, the dynamic stress drop or the rupture directivity. Nevertheless, its simplicity and first order accuracy suggest that the model can yield plausible and useful estimates of the landslide distribution area in near-real time, with earthquake parameters issued by standard detection routines.

Topographic Types Influence on the Landslide Movement

2011 ◽  
Vol 243-249 ◽  
pp. 5258-5262 ◽  
Author(s):  
Xiao Yi Fan ◽  
Meng Han
Keyword(s):  
Friction Coefficient ◽  
Power Law ◽  
Field Investigation ◽  
Seismic Landslides ◽  
Landslide Volume ◽  
Influence Coefficients ◽  
Topographic Influence ◽  
Landslide Movement

According to the 95 landslides of field investigation and literatures, the topographic types of landslide movement were divided into river, ladder and linearity. Based on the power-law relationship between the volume and equivalent friction coefficient of non-block landslides, the topographic influence coefficients were studied which were influenced by the landslide volumes and occurrence mechanisms. Because of different volumes of seismic landslides and rainfall landslides, the influence coefficients of topography were significant different. It indicated that the disaster-causing mechanism of landslides not only closely related with the landslide volume, but also were controlled by topographic types and occurrence mechanisms.

Analysis of factors affecting the Spatial Distribution of co-seismic landslides triggered by the 2011 (Mw 6.9) Sikkim earthquake

2021 ◽  
Author(s):  
Saurav Kumar ◽  
Sengupta Aniruddha
Keyword(s):  
Slope Failure ◽  
Slope Angle ◽  
Slope Aspect ◽  
Susceptibility Map ◽  
Past Study ◽  
Topographic Wetness Index ◽  
Landslide Occurrence ◽  
Seismic Landslides ◽  
Sikkim Earthquake ◽  
Seismic Landslide

&lt;p&gt;The Himalayan region is known as an earthquake-triggered landslides prone area. It is characterized by high seismicity, large relative relief, steep slopes, and dense precipitation. These seismically triggered landslides are likely to affect substantial societal impacts, including loss of life, damage to houses, public buildings, various lifeline structures like highways, railways tracks, etc. Further, they obstruct post-earthquake emergency response efforts. A past study by Martha et al. 2014 reported that an earthquake of Mw 6.9 in 2011 triggered 1196 landslides in Sikkim which is a part of the eastern Himalayas. The slope failure events are controlled by several factors, which can be grouped into four main classes: seismology, topography, lithology, and hydrology. Each class contains several sub-factors. Having in-depth knowledge of these factors and their influence on the density of landslide events in the affected area due to the 2011 Sikkim earthquake is essential to realize the level of threat of co-seismic landslide due to future earthquakes. Eight landslide controlling factors is considered in this analysis including peak ground acceleration (PGA), slope, aspect, elevation, curvature, lithology, distance from rivers, and topographic wetness index (TWI). Further, the frequency ratio model using the GIS framework is applied to evaluate the contribution of each landslide controlling factor to landslide occurrence. Scatter plots between the number of landslides per km&lt;sup&gt;2&lt;/sup&gt; (LN) and percentage of landslide area (LA) and causative factors indicate that distance from the river, slope angle, and PGA are the dominant factors that control the landslides. The results of the above analysis showed that the majority of co-seismic landslides occurred at slope &gt;30&amp;#176;, preferably in East, Southeast, and South directions and near river within a distance of 1500 m. The detailed study of interactions among these factors can improve the understanding of the mechanisms of co-seismic landslide occurrence in Sikkim and will be useful for producing a co-seismic landslide susceptibility map of the area.&lt;/p&gt;

Background Factors Analysis and Risk Evaluation Based on Fuzzy Theory in Wenchuan Seismic Landslides

2012 ◽  
Vol 204-208 ◽  
pp. 2698-2704
Author(s):  
Meng Han ◽  
Xiao Yi Fan ◽  
Jian Ping Qiao
Keyword(s):  
Risk Evaluation ◽  
Fuzzy Theory ◽  
Contribution Rate ◽  
Internal Factors ◽  
Background Factors ◽  
Seismic Landslides ◽  
Factors Analysis ◽  
Principle Of Maximum ◽  
Slope Direction ◽  
Area And Volume

Earthquake is one of the most important external factors causes landslide, the geological and topographical conditions of the slope itself is the most important internal factors which induce landslide on landslide’s size and probability. These factors can be described as background factors impact the seismic landslides. Through the analysis and statistics on seismic landslides’ data in this paper, five directions were studied. These are lithology, slope height, slope angel, slope shape and slope direction. The influence features on number, area and volume of the five background factors show the contribution rate and risk degree. Using fuzzy theory to carry out quantitative and semi-quantitative analysis on these factors, the weights of the five background factors can be determined. Then using the risk evaluation on a specific landslide according to the principle of maximum membership, the result shows this method is reasonable.

scholarly journals Monitoring and Assessment for the Susceptibility of Landslide Changes After the 2017 Ms 7.0 Jiuzhaigou Earthquake Using the Remote Sensing Technology

2021 ◽  
Vol 9 ◽  
Author(s):  
Xinyi Guo ◽  
Bihong Fu ◽  
Jie Du ◽  
Pilong Shi ◽  
Jingxia Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Impact Factors ◽  
Support Vector ◽  
Temporal Characteristics ◽  
Mountainous Regions ◽  
Seismic Landslides ◽  
Coseismic Landslide ◽  
Spatio Temporal ◽  
Seismic Landslide ◽  
The Impact

Monitoring the change of post-seismic landslides could provide valuable information for geological disaster treatment. The 2017 Jiuzhaigou Ms 7.0 earthquake has triggered a large number of landslides in the Jiuzhaigou United Nations Educational, Scientific and Cultural Organization (UNESCO) Natural Heritage site, which provides a unique opportunity for monitoring the spatio-temporal characteristics and exploring the impact factors of post-seismic landslides change. In this study, the spatio-temporal characteristics of landslides and their post-seismic changes are analyzed using multi-source, multi-temporal, and multi-scale remote sensing data combining with the field study. The Support Vector Machine classification, visual interpretation, field investigation, and Geographic Information System technology are employed to extract landslides and analyze their spatial distribution patterns. Moreover, the Certainty Factor method is used to explore the susceptibility of landslides and to find key impact factors. Our results show that the net increase area of landslide is 1.2 km2 until September 27th, 2019, which are induced by the expansion of coseismic landslide, the post-seismic landslide, and the expansion of vegetation degradation. Moreover, the area expansion of the coseismic and post-seismic landslides is mainly related to the increase of debris flow induced by the post-seismic torrential rainfalls. The highest net increase rate of post-seismic landslide change does not distribute on the regions with the highest density of coseismic landslides. The susceptibility of post-seismic landslide change is greatly influenced by slope, altitude, aspect, peak ground acceleration fault, and strata. It is higher in the coseismic landslide area with low susceptibility. This study also suggests that the potential landslides will most likely occur in the unstable slope region affected by the additional driving force. Therefore, great attention should be paid to identify and prevent the potential landslides on unstable slopes in addition to treatments of the sliding slopes. This study provides a good example for the monitoring and assessment of post-seismic landslides in mountainous regions with a steep slope and deep valley.

scholarly journals Application of a Three-Dimensional Deterministic Model to Assess Potential Landslides, a Case Study: Antong Hot Spring Area in Hualien, Taiwan

Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 480 ◽  
Author(s):  
Lien-Kuang Chen ◽  
Chih-Hsin Chang ◽  
Che-Hsin Liu ◽  
Jui-Yi Ho
Keyword(s):  
Empirical Equation ◽  
Hot Spring ◽  
Three Dimensional ◽  
In Situ Monitoring ◽  
Landslide Area ◽  
Landslide Volume ◽  
Proposed Model ◽  
Spring Area ◽  
Area And Volume

This study proposes a landslide disaster assessment model combining a fully three-dimensional, physically-based landslide model with high precision of in situ survey data such as surface slip signs, geologic drilling results, underground water observation, and displacement monitoring results over time to perform distribution of potential landslide zones and the size of landslides (area and volume) in the Antong hot spring area in Hualien, Taiwan. The distribution of potential landslide zones in the study area was represented by slope stability safety factors. The results of the analysis showed that the toe of the slope and two upward slopes in the study area were potential landslide areas with safety factors of 1.37, 0.92, and 1.19, respectively. The 3D model analysis results indicated that a landslide could occur at a depth of 20 m at the toe of the slope. Monitoring results for 2015 and 2016 showed that the sliding depth at the toe of the slope was approximately 22.5 m; consequently, the error of landslide depth was only 2.5 m. The simulated results and in situ monitoring results were in good agreement. In addition, the simulated landslide volume was also compared with the results of an empirical equation commonly used in Taiwan to determine their differences. The landslide volumes estimated using the empirical equation were only approximately 38.5% in zone 1, 42.9% in zone 2, and 21.7% in zone 3 of that generated by the proposed model. The empirical equation was used to calculate the landslide volume according to the landslide area, which was subsequently converted into landslide depth. However, the obtained landslide depth was considerably lower than that derived from the in situ monitoring, implying that an empirical estimation approach may result in serious underestimation. Thus, the proposed model could predict landslide area and volume in advance to assist authorities in minimizing loss of life and property damage during a heavy rainfall event.

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