Norwegian Geotechnical Institute (NGI) Digital Archive
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Soil Metabolome Impacts the Formation of the Eco-corona and Adsorption Processes on Microplastic Surfaces
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Ground Investigations and Detection and Monitoring of Landslides Using SAR Interferometry in Gangtok, Sikkim Himalaya
Get PDFThe Himalayan state of Sikkim is prone to some of the world’s largest landslides, which have caused catastrophic damage to lives, properties, and infrastructures in the region. The settlements along the steep valley sides are particularly subject to frequent rainfall-triggered landslide events during the monsoon season. The region has also experienced smaller rock slope failures (RSF) after the 2011 Sikkim earthquake. The surface displacement field is a critical observable for determining landslide depth and constraining failure mechanisms to develop effective mitigation techniques that minimise landslide damage. In the present study, the persistent scatterers InSAR (PSI) method is employed to process the series of Sentinel 1-A/B synthetic aperture radar (SAR) images acquired between 2015 and 2021 along ascending and descending orbits for the selected areas in Gangtok, Sikkim, to detect potentially active, landslide-prone areas. InSAR-derived ground surface displacements and their spatio-temporal evolutions are combined with field investigations to better understand the state of activity and landslide risk assessment. Field investigations confirm the ongoing ground surface displacements revealed by the InSAR results. Some urban areas have been completely abandoned due to the structural damage to residential housing, schools, and office buildings caused by displacement. This paper relates the geotechnical investigations carried out on the ground to the data obtained through interferometric synthetic aperture radar (InSAR), focusing on the triggering mechanisms. A strong correlation between seasonal rainfall and landslide acceleration, as well as predisposing geological-structural setting, suggest a causative mechanism of the landslides.Ground Investigations and Detection and Monitoring of Landslides Using SAR Interferometry in Gangtok, Sikkim HimalayapublishedVersio
Photophysiological responses of bottom sea-ice algae to fjord dynamics and rapid freshening
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Industrially relevant pyrolysis of diverse contaminated organic wastes: Gas compositions and emissions to air
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Deformation triggers and stability evolution of landslide from multiple observations
Get PDFExternal causes like changes in reservoir level and intense rainfall can cause reservoir landslides. Exploring the factors that govern landslide deformation and analyzing its stability evolution is essential in mitigating the associated risks. The Sanzhouxi landslide, which has experienced ongoing movements and has been implemented a professional monitoring system, is chosen for analysis in this paper. A combination of geological survey and analysis of monitoring data is utilized to explore landslide deformation characteristics. A data mining method, grey relation analysis (GRA), is subsequently performed to determine the causes of landslide deformation. Furthermore, the stability of the Sanzhouxi landslide in response to reservoir level fluctuation and rainfall for each day over an entire year is assessed using the Morgenstern-Price (MP) approach in 2D GeoStudio software. Such a process illustrates clearly how the landslide stability alters with external triggers changing. The findings reveal that the landslide deforms variably in spatial and temporal. The reservoir level rising contributes to landslide deformation primarily, while rainfall has a secondary impact. The factor of safety (FS) of the Sanzhouxi landslide drops from 1.17 to 1.07 during high reservoir water level periods and remain the same or increase in other periods except for some transitory moments while decreasing only by about 2% under the effect of rainfall. The daily FS results validate the dominant influence of reservoir level fluctuation on the stability of the landslide. The comprehensive understanding of landslide movement based on deformation characteristics, triggering factor identification, and daily stability validation, contributes to realizing nearly realtime prediction and evaluating the risk due to slope movements in similar geological settings worldwide.Deformation triggers and stability evolution of landslide from multiple observationspublishedVersio
Geotechnical characterization of index and deformation properties of Stockholm clays
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Direction Estimates for Short‐Period P‐Waves on Three‐Component Stations and Arrays
Get PDFP‐arrival backazimuth estimates can be crucial in locating poorly constrained seismic events. Correlating short windows of the vertical waveform with corresponding windows of the radial rotation for different backazimuths can provide estimates, but these are often uncertain and biased due to skewness in the Z–R correlation functions. Assessing how well cosine curves centered on different backazimuths match the Z–R correlation functions provides more reliable estimates that depend less upon the time‐window used. Stacking best‐fit‐cosine curves from neighboring three‐component stations improves stability further in a form of array‐processing that does not require coherence between the waveforms themselves. We demonstrate for recordings of North Korean nuclear tests at the Pilbara 3C array in Australia that the biases in the Z–R correlation functions vary greatly between adjacent stations. This bias is reduced both by the cosine curve fitting and stacking operations. We advocate obtaining backazimuth estimates for all P arrivals at three‐component stations globally. This could improve phase association and event location, identify sensor orientation problems, and provide baseline backazimuth corrections and uncertainty estimates. We propose two benchmark datasets for developing, documenting, and comparing backazimuth estimation algorithms and codes. All the data and code used to generate the results presented here are open.Direction Estimates for Short‐Period P‐Waves on Three‐Component Stations and ArrayspublishedVersio
