Glacio-marine clay resistivity as a proxy for remoulded shear strength: correlations and limitations

Long, Michael; Pfaffhuber, Andreas Aspmo; Bazin, Sara; Kåsin, Kristoffer; Gylland, Anders Samstad; Montaflia, Alberto

doi:10.1144/qjegh2016-136

Cited by 9 publications

(4 citation statements)

References 45 publications

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“…5) show more spread than Long et al. (2017), but unlike their data set that consisted exclusively of marine clay, our lab samples included other types of sediment. Even though we observed some systematic bias in our airborne electromagnetic (AEM)‐derived resistivity models compared with resistivity CPT (RCPT) measurements, the AEM generally has the same strengths and weaknesses of in situ RCPT in terms of identifying quick and non‐brittle material.…”

Section: Discussionmentioning

confidence: 82%

“…Our exploratory data analysis confirms that the petrophysical relationship between remoulded shear strength and resistivity of glaciomarine clay at our study site is consistent with observations at other locations in Scandinavia. Long et al.’s (2017) review showed the same general trends throughout sites in Norway: material below 10 Ωm was never quick and only occasionally brittle; and material between 10 and 100 Ωm could either be quick clay or stable, unleached marine clay mixed with coarser sediment. Our data (Fig.…”

Section: Discussionmentioning

confidence: 90%

“…Long et al. (2017) provide a comprehensive overview of resistivity signatures from 30 sites across Norway showing the correlation between increased resistivity and reduced remoulded shear strength. Using resistivity as an indicator for quick clay presence provides the opportunity to use airborne electromagnetic (AEM) methods to scan for quick clay occurrences efficiently on a regional scale.…”

Section: Introductionmentioning

confidence: 99%

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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: Discussionmentioning

confidence: 82%

Section: Discussionmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

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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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“…However, the resistivity signature of quick clay is non-unique. Whereas material that with very high (>100 Ωm) or very low (<5 Ωm) can be ruled out as quick, marine clay with intermediate resistivities may either be leached, quick marine clays, or marine clays that was mixed with coarser sediments or which has undergone weathering processes [46]. Intrusive geotechnical investigations are needed to resolve this ambiguity.…”

Section: Quick Clay Modellingmentioning

confidence: 99%

AEM in Norway: A Review of the Coverage, Applications and the State of Technology

Harrison¹,

Baranwal

Pfaffhuber³

et al. 2021

Remote Sensing

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From the first use of airborne electromagnetic (AEM) systems for remote sensing in the 1950s, AEM data acquisition, processing and inversion technology have rapidly developed. Once used extensively for mineral exploration in its early days, the technology is increasingly being applied in other industries alongside ground-based investigation techniques. This paper reviews the application of onshore AEM in Norway over the past decades. Norway’s rugged terrain and complex post-glacial sedimentary geology have contributed to the later adoption of AEM for widespread mapping compared to neighbouring Nordic countries. We illustrate AEM’s utility by using two detailed case studies, including time-domain and frequency domain AEM. In both cases, we combine AEM with other geophysical, geological and geotechnical drillings to enhance interpretation, including machine learning methods. The end results included bedrock surfaces predicted with an accuracy of 25% of depth, identification of hazardous quick clay deposits, and sedimentary basin mapping. These case studies illustrate that although today’s AEM systems do not have the resolution required for late-phase, detailed engineering design, AEM is a valuable tool for early-phase site investigations. Intrusive, ground-based methods are slower and more expensive, but when they are used to complement the weaknesses of AEM data, site investigations can become more efficient. With new developments of drone-borne (UAV) systems and increasing investment in AEM surveys, we see the potential for continued global adoption of this technology.

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A methodology for mapping of quick clay in Sweden

et al. 2022

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Landslides may cause severe destruction that affects both the individuals and functions vital for society. Minor landslides in an area with quick clay may trigger secondary slides, influencing a much greater area compared to slides in areas with no quick clay. Today’s expanding societies demand new areas for exploitation. To effectively meet this demand, there is an increased need to identify areas where quick clay may occur. Direct or indirect methods for assessing the presence of quick clay have previously been presented as well as a strategy for site investigations in quick clay areas. In this article, a methodology for mapping quick clays for the Swedish conditions with methods commonly available in this area is presented. The methodology presented in the article is structured in steps with different levels of detail and visualized with two conceptual flowcharts. Depending on the stage of planning, different types of surveys are recommended. The methodology has been applied at four sites where integrated interpretation of airborne and ground geophysical measurements as well as geotechnical investigations have been carried out. The results from two of these sites are presented here. The study reveals that all the methods used have their advantages and limitations. However, a combined use of the information provides much more accurate interpretation that can be used for a more cost-effective future planning and decision-making.

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Glacio-marine clay resistivity as a proxy for remoulded shear strength: correlations and limitations

Cited by 9 publications

References 45 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

AEM in Norway: A Review of the Coverage, Applications and the State of Technology

A methodology for mapping of quick clay in Sweden

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