Quick-Clay Hazard Mapping in Norway

Havnen, Ingrid; Ottesen, Hanne Bratlie; Haugen, Ellen D.; Frekhaug, Martine H.

doi:10.1007/978-3-319-56487-6_50

Cited by 3 publications

(1 citation statement)

References 3 publications

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“…In Norway, major slides occur regularly with a return period of more than one in ten years (Nadim et al., 2008). It is thus important to map and delineate sensitive clay deposits, not only for individual construction projects, but also for regional‐ and national‐scale hazard assessment (Havnen et al., 2017).…”

Section: Introductionmentioning

confidence: 99%

A machine learning–based approach to regional‐scale mapping of sensitive glaciomarine clay combining airborne electromagnetics and geotechnical data

Christensen¹,

Harrison²,

Pfaffhuber³

et al. 2021

Near Surface Geophysics

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Sensitive glaciomarine clays, often referred to as ‘quick clay’, commonly occur in many countries at high, northerly latitudes, causing frequent and occasionally devastating landslides. The salt content of quick clay is strongly correlated to both its shear strength and electrical resistivity. Hence, it can be mapped using electromagnetic methods more efficiently than traditional intrusive methods, the latter of which can often be slow and costly. However, the resistivity signature of quick clay is non‐unique, leading to ambiguous, imprecise interpretations of geophysical models. In this study, we present an improved method for predicting the probability of quick clay using airborne electromagnetics. Using machine learning algorithms, we combine geophysical models with geotechnical data to address the issue of their non‐unique resistivity signature. Beyond resistivity values, the machine learning algorithms use spatial derivatives of resistivity and spatial attributes. We evaluate the performance of this method using data collected from a road construction project in central Norway. Results show that this method is able to make plausible and accurate predictions of quick clay occurrence using as few as 10 boreholes across an area of 14.8 km2, and that it outperforms a simple interpretation based on resistivity intervals alone. In addition to a ‘best guess’ categorical classification, these algorithms output probability estimates, and we demonstrate that they are a reliable indication of uncertainty. The accuracy of these predictions also tends to increase as more geotechnical data are included as training data, helping compensate for the limited resolution of the airborne electromagnetics data. Given that the petrophysics of the clays at this test site are consistent with observations in other regions, we expect this method has the potential to make quick clay hazard mapping more efficient by offering valuable early‐phase insights, leading to large time and cost savings for both infrastructure planning and regional hazard mapping.

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Section: Introductionmentioning

confidence: 99%

A machine learning–based approach to regional‐scale mapping of sensitive glaciomarine clay combining airborne electromagnetics and geotechnical data

Christensen¹,

Harrison²,

Pfaffhuber³

et al. 2021

Near Surface Geophysics

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Improving intuitiveness in geoscience hazard maps: a web-based experiment supporting governmental map development

Bang-Kittilsen,

Midtbø

2024

Cartography and Geographic Information Science

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Estimating the volume of the 1978 Rissa quick clay landslide in Central Norway using historical aerial imagery

et al. 2022

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Quick clay is found across Scandinavia and is especially prominent in south-eastern and central Norway. Quick clay is prone to failure and can cause landslides with high velocities and large run-outs. The 1978 Rissa landslide is one of the best-known quick clay landslides to have occurred in the last century, both due to its size and the fact that it was captured on film. In this article, we utilise Structure from Motion Multi-View Stereo (SfM-MVS) photogrammetry to process historical aerial photography from 1964 to 1978 and derive the first geodetic volume of the Rissa landslide. We found that the landslide covered a total onshore area of 0.36 km2 and had a geodetic volume of 2.53 ± 0.52 × 106 m3 with up to 20 m of surface elevation changes. Our estimate differs profusely from previous estimates by 43–56% which can partly be accounted for our analysis not being able to measure the portion of the landslide that occurred underwater, nor account for the material deposited within the landslide area. Given the accuracy and precision of our analyses, we believe that the total volume of the Rissa landslide may have been less than originally reported. The use of modern image processing techniques such as SfM-MVS for processing historical aerial photography is recommended for understanding landscape changes related to landslides, volcanoes, glaciers, or river erosion over large spatial and temporal scales.

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Quick-Clay Hazard Mapping in Norway

Cited by 3 publications

References 3 publications

A machine learning–based approach to regional‐scale mapping of sensitive glaciomarine clay combining airborne electromagnetics and geotechnical data

A machine learning–based approach to regional‐scale mapping of sensitive glaciomarine clay combining airborne electromagnetics and geotechnical data

Improving intuitiveness in geoscience hazard maps: a web-based experiment supporting governmental map development

Estimating the volume of the 1978 Rissa quick clay landslide in Central Norway using historical aerial imagery

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