Landslides pose significant risks to lives and infrastructure. This section explores the factors influencing slope stability, including geological conditions and triggering mechanisms. Understanding these elements is crucial for assessing and mitigating landslide hazards.
Geophysical methods and remote sensing techniques play vital roles in landslide investigation. We'll examine how seismic refraction, electrical resistivity surveys, and GIS-based assessments help identify vulnerable areas and monitor slope movements, enhancing our ability to predict and manage landslide risks.
Slope Stability Factors and Landslide Triggers
Geological and Geomorphological Factors
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Slope stability is influenced by a combination of geological, geomorphological, and hydrological factors, such as rock and soil properties, slope geometry, groundwater conditions, and external forces
The shear strength of the slope materials, which is determined by cohesion and internal friction, plays a crucial role in resisting the driving forces and maintaining slope stability
The presence of weak layers, such as clay seams or bedding planes, can create potential slip surfaces and increase the susceptibility to landslides
Slope geometry factors include slope angle, height, and shape, which affect the balance between driving and resisting forces
Triggering Mechanisms and External Forces
Landslides can be triggered by various mechanisms, including intense rainfall, earthquakes, volcanic activity, human-induced changes in slope geometry or loading, and changes in groundwater conditions
Groundwater conditions, particularly pore water pressure, can significantly influence slope stability by reducing the effective stress and shear strength of the slope materials
External forces, such as seismic ground motion or surcharge loads from construction activities, can trigger landslides by increasing the driving forces or reducing the resisting forces
The concept of factor of safety (FoS) is used to quantify slope stability, with FoS > 1 indicating a stable slope and FoS < 1 indicating an unstable slope
FoS is calculated as the ratio of resisting forces (shear strength) to driving forces (shear stress) acting on the slope
A FoS of 1.5 or higher is generally considered acceptable for long-term slope stability
Geophysical Methods for Landslide Investigation
Seismic Refraction Surveys
Seismic refraction surveys can provide information on the subsurface velocity structure, allowing the identification of weak layers, bedrock depth, and groundwater table
The seismic velocity contrast between the landslide mass and the underlying bedrock can help delineate the landslide geometry and thickness
The presence of low-velocity zones may indicate areas of higher fracture density or water saturation, which are more susceptible to landslides
Seismic refraction data is acquired using an array of geophones and a seismic source (sledgehammer or explosives), and the travel times of refracted waves are analyzed to create a velocity model
Electrical Resistivity Surveys
Electrical resistivity surveys can map the subsurface resistivity distribution, which is sensitive to variations in lithology, water content, and clay content
Low resistivity zones may indicate the presence of water-saturated or clay-rich materials, which are often associated with landslide-prone areas
Time-lapse electrical resistivity monitoring can detect changes in water content or the development of slip surfaces over time
Electrical resistivity data is collected using an array of electrodes and a resistivity meter, and the data is inverted to create a 2D or 3D resistivity model
The integration of multiple geophysical methods, such as seismic refraction and electrical resistivity, can provide complementary information and improve the reliability of landslide characterization
Geophysical data should be interpreted in conjunction with geological and geotechnical data to develop a comprehensive understanding of the landslide system
Remote Sensing and GIS in Landslide Analysis
Remote Sensing Techniques for Landslide Mapping
Remote sensing techniques, such as aerial photography, satellite imagery, and LiDAR, can provide high-resolution data for landslide mapping and monitoring
Stereoscopic aerial photographs can be used to identify and delineate landslide features, such as scarps, cracks, and displaced masses
High-resolution satellite imagery (Quickbird, WorldView) can detect surface deformation and changes in vegetation cover associated with landslides
LiDAR data can generate detailed digital elevation models (DEMs) to analyze slope morphology and identify subtle topographic features related to landslides
Interferometric Synthetic Aperture Radar (InSAR) can measure ground deformation with millimeter-level accuracy, allowing the detection of slow-moving landslides
GIS-based Landslide Susceptibility Assessment
Geographic Information Systems (GIS) provide a powerful platform for integrating, analyzing, and visualizing multi-source data for landslide susceptibility assessment
GIS can be used to create landslide inventory maps by compiling historical records, field observations, and remote sensing data
Landslide susceptibility maps can be generated by integrating factors such as slope, lithology, land use, and hydrological conditions using GIS-based statistical or machine learning models (logistic regression, random forest)
GIS allows the overlay and spatial analysis of different thematic layers to identify areas with high landslide susceptibility and risk
The combination of remote sensing and GIS enables the rapid and cost-effective mapping and monitoring of landslides over large areas, particularly in remote or inaccessible regions
Case Studies of Significant Landslides
2014 Oso Landslide, Washington, USA
The 2014 Oso landslide in Washington, USA, highlighted the importance of understanding the geologic history and the presence of weak, glacially-derived sediments in slope stability assessment
The landslide occurred in a region with a history of landslides and was triggered by heavy rainfall and groundwater saturation
Geophysical investigations, including seismic refraction and electrical resistivity, revealed the presence of a low-velocity, high-conductivity layer at the base of the landslide, which likely acted as a slip surface
The landslide resulted in 43 fatalities and caused significant damage to infrastructure and property
2017 Xinmo Landslide, Sichuan, China
The 2017 Xinmo landslide in Sichuan, China, demonstrated the role of seismic shaking and topographic amplification in triggering massive rock avalanches
The landslide was triggered by a nearby earthquake and involved the collapse of a large volume of fractured rock mass along a steep mountain slope
Seismic monitoring data indicated that topographic amplification of seismic waves along the ridge may have contributed to the failure
The landslide buried a village, resulting in over 100 fatalities and significant destruction
1963 Vajont Landslide, Italy
The 1963 Vajont landslide in Italy is a classic example of a reservoir-induced landslide caused by the impoundment of a dam
The landslide occurred on a pre-existing, slow-moving landslide that accelerated due to the increase in pore water pressure and the reduction of effective stress caused by the reservoir filling
Geotechnical investigations, including borehole drilling and inclinometer measurements, revealed the presence of a clay layer acting as the sliding surface
The landslide generated a massive wave that overtopped the dam, causing extensive damage and loss of life downstream
The analysis of case studies emphasizes the importance of a multi-disciplinary approach, integrating geological, geotechnical, and geophysical data, for understanding the complex mechanisms and factors controlling landslides