Skred är en av de naturliga processer som formar landskapet omkring oss. De kan dock, om de sker ibebyggda områden, orsaka stor skada på byggnader, infrastruktur och utgöra en fara för människors liv.Det är därför viktigt att kunna identifiera områden där skred potentiellt kan ske så att lämpliga åtgärderkan vidtas i tid. En modell har tagits fram av Statens geotekniska institut (SGI) och Sveriges geologiskaundersökning (SGU) för att ge en första indikation på var skred kan ske. Modellen baseras på en algoritmav Tryggvason et al. (2015) där områden som består av finkorniga jordarter och där lutningsförhållandetär minst 1:10 betraktas som skredkänsliga. Syftet med denna studie var att undersöka till vilken gradmodellens aktsamhetsområden sammanfaller med tidigare skred från SGI och SGUs databaser. 90 - 94% av de tidigare skreden överlappade med modellerade aktsamhetsområden. Möjliga anledningar till attskred hamnat utanför dessa områden undersöktes även. Att de befinner sig i en jordart som inte betraktassom skredkänslig och att lutningen numera är lägre än tröskelvärdet för modellen var de huvudsakligaanledningarna till detta. / A landslide is a type of mass movement down a slope. It is a natural part of the evolution of the landscapearound us but can cause extensive damages on buildings, infrastructure and pose a threat to human livesif they occur in populated areas. It is therefore important to know which areas that are prone to landslidesso that appropriate measures can be taken in time. It is possible to calculate how stable a certain soil isby taking samples of it and testing it in the lab. In these tests it is determined how sensitive the soil is tovibrations, a higher water content and/or if it is remolded. However, this takes time and require a lot ofwork. Since not all soil types are equally sensitive these tests do not have to be performed on everyslope, but it is important that the most sensitive areas are not overlooked. For this reason, a model hasbeen developed that displays areas where the slope stability might need to be examined prior, forexample, larger infrastructure projects are started. From previous studies it has been found thatlandslides mostly occur in fine grained soils, like silt and clay, and where the slope is steeper than 5.7degrees. Areas that consist of any of these soil types and has a slope over this threshold are consideredpotentially sensitive to landslides in the model. The purpose of this study was to assess how well themodel is at identifying areas that might be prone to landslides. This was done by determining how manyof previous landslides, that are registered in two databases, that fall within the areas marked aspotentially sensitive. Why some landslides were located outside of these areas was also examined. Themain reasons were that the soil type the landslide occurred in is not considered sensitive by the modelor the inclination of the slope have changed since the landslide occurred. 90 - 94 % of the previouslandslides were found to be located within areas that the model point out as potentially sensitive.

LiDAR (Light Detection and Ranging) elevation data were collected in the Panther Creek Watershed, Yamhill County, Oregon in September and December, 2007, March, 2009 and March, 2010. LiDAR derived images from the March, 2009 dataset were used to map pre-historic, historic, and active landslides. Each mapped landslide was characterized as to type of movement, head scarp height, slope, failure depth, relative age, and direction. A total of 153 landslides were mapped and 81% were field checked in the study area. The majority of the landslide deposits (127 landslides) appear to have had movement in the past 150 years. Failures occur on slopes with a mean estimated pre-failure slope of 27° ± 8°. Depth to failure surfaces for shallow-seated landslides ranged from 0.75 m to 4.3 m, with an average of 2.9 m ± 0.8 m, and depth to failure surfaces for deep-seated landslides ranged from 5 m to 75m, with an average of 18 m ± 14 m. Earth flows are the most common slope process with 110 failures, comprising nearly three quarters (71%) of all mapped deposits. Elevation changes from two of the successive LiDAR data sets (December, 2007 and March, 2009) were examined to locate active landslides that occurred between the collections of the LiDAR imagery. The LiDAR-derived DEMs were subtracted from each other resulting in a differential dataset to examine changes in ground elevation. Areas with significant elevation changes were identified as potentially active landslides. Twenty-six landslides are considered active based upon differential LiDAR and field observations. Different models are used to estimate landslide susceptibility based upon landslide failure depth. Shallow-seated landslides are defined in this study as having a failure depth equal to less than 4.6 m (15 ft). Results of the shallow-seated susceptibility map show that the high susceptibility zone covers 35% and the moderate susceptibility zone covers 49% of the study area. Due to the high number of deep-seated landslides (58 landslides), a deep-seated susceptibility map was also created. Results of the deep-seated susceptibility map show that the high susceptibility zone covers 38% of the study area and the moderate susceptibility zone covers 43%. The results of this study include a detailed landslide inventory including pre-historic, historic, and active landslides and a set of susceptibility maps identifying areas of potential future landslides.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第17868号 / 工博第3777号 / 新制||工||1577(附属図書館) / 30688 / 京都大学大学院工学研究科社会基盤工学専攻 / (主査)教授 寶 馨, 教授 木村 亮, 准教授 立川 康人 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第18563号 / 工博第3924号 / 新制||工||1603(附属図書館) / 31463 / 京都大学大学院工学研究科社会基盤工学専攻 / (主査)教授 藤田 正治, 教授 中川 一, 准教授 竹林 洋史 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第19285号 / 工博第4082号 / 新制||工||1629(附属図書館) / 32287 / 京都大学大学院工学研究科社会基盤工学専攻 / (主査)教授 寶 馨, 教授 角 哲也, 准教授 佐山 敬洋 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

京都大学 / 0048 / 新制・課程博士 / 博士(理学) / 甲第19955号 / 理博第4222号 / 新制||理||1607(附属図書館) / 33051 / 京都大学大学院理学研究科地球惑星科学専攻 / (主査)教授 釜井 俊孝, 准教授 王 功輝, 教授 林 愛明 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DGAM

More than 100 landslides have been mapped along the southeast flank of the Uinta Mountains. Large landslide deposits are up to 4.6 kilometers long and have an area of approximately 5-9 km². Landslide types include multiple and successive rock slumps, debris slumps and debris flows. Most landslides have a main head scarp in the Bishop Conglomerate and the large landslides have many minor scarps. Multiple slump blocks are manifest by repeated transverse ridges and trenches in the head area of some landslides. Most body and toe areas are deeply incised by gully erosion (up to 91 meters deep) and drainages are well developed with little ponding. Detailed mapping of the large landslides shows that the deposits are an accumulation of successive slope failures that have continually eroded the landscape over time. Many landslides in the area appear to be inactive and dormant but slopes may continue to fail particularly if landslides are disturbed. A Geographic Information System (GIS) was used to analyse slope failing factors and the main factor that seems to have contributed to slope failure is the presence of abundant shale-rich, weak bedrock capped with the thick and fairly resistant Bishop Conglomerate. Slopes are further destabilized as water percolates down through the porous Bishop Conglomerate. Eventually the water meets underlying shale-rich bedrock where it is channelled near this contact until it emerges as springs. This groundwater flow likely reduces shear strength of the shale-rich substrate and of some of the finer grained layers in the Bishop Conglomerate. Other important slope failure factors include the removal of easily erodable Mesozoic shales from beneath the more-resistant Bishop Conglomerate, headward gully erosion, bedrock dip and slope aspect.

An integration of geological and geophysical techniques has been used to characterize the internal structure of the Little Valley Landslide in Draper, Utah, USA. The Little Valley Landslide is a pre-historic landslide as old as 13ka B.P. It is found to consist of chaotic and disturbed weathered volcanic units derived from Tertiary age volcanics that comprise a great portion of the Wasatch Range. Geotechnical investigations that were integrated with the geophysical results included excavation of trenches and drilling of boreholes. Geophysical methods, in particular high-resolution seismic data, were used to provide a framework for interpreting the geotechnical observations. High-resolution seismic reflection data, seldom used in landslide investigations, were acquired and processed in order to image the basal or glide surface of the landslide and the structure underlying the landslide. The integration of the geotechnical and geophysical investigations provided a better understanding of the geometry of a portion of the Little Valley Landslide. Trenching and drilling identified landslide material in the subsurface. The high-resolution seismic reflection data imaged the glide surface with the onset of coherent reflectivity. A decollement or glide surface underlies the landslide indicating a large mass movement. The glide surface is observed on the seismic reflection profiles to be deepest in the center portion of the landslide. It is observed in the seismic reflection images to shallow up slope and creating a trough-like shape feature. A contour map modeling the middle of the Little Valley Landslide is derived from the seismic data. This study shows that seismic reflection techniques can be successfully used in complex alpine landslide regions. They are also efficient and cost-effective tools when compared to trenching and drilling investigations. The seismic data can (1) provide a framework to link geological data and (2) take the place of an extensive trenching and drilling program.

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