Geophysical Anatomy of the Hollin Hill Landslide, North Yorkshire, UK

Chambers, Jonathan; Weller, A.; Gunn, David; Kuras, O.; Wilkinson, P.B.; Meldrum, P.I.; Ogilvy, R.D.; Jenkins, Gareth; Gibson, Andy; Ford, Jonathan R.; Price, S.J.

doi:10.3997/2214-4609.20146272

Cited by 6 publications

(5 citation statements)

References 4 publications

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“…Newton of end tipping wagons and operations in the vicinity of the site at the time of construction). The embankment has been subject to several phases of site investigation spanning from September 2005, which has included drilling, collection of core samples, invasive probing and non-invasive geophysical surveying [16][17][18][19][8][9][10][11]; borehole locations and the resistivity lines are shown Fig. 4.…”

Section: East Leake Sitementioning

confidence: 99%

Moisture monitoring in clay embankments using electrical resistivity tomography

Gunn

Chambers

Uhlemann

et al. 2015

Construction and Building Materials

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104

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Section: East Leake Sitementioning

confidence: 99%

Moisture monitoring in clay embankments using electrical resistivity tomography

Gunn

Chambers

Uhlemann

et al. 2015

Construction and Building Materials

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104

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“…Many tectonically stable regions suffer from significant ground motion due to the effects of former coalfields (McCay et al., 2018), landslides (Chambers et al., 2008), the shrink and swell of shallow clays (Aldiss et al., 2014; Crilly, 2001), tree growth, coastal erosion, natural sinkholes (Banks et al., 1995; Lamont‐Black et al., 2002), and tunneling (e.g., Crossrail, [Milillo et al., 2018]). Ground motion analysis has recently focused on satellite‐based InSAR, which uses the phase difference between pairs of radar satellite images to map ground deformation at mm/yr precision.…”

Section: Introductionmentioning

confidence: 99%

Time‐Series Prediction Approaches to Forecasting Deformation in Sentinel‐1 InSAR Data

et al. 2021

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Time series of displacement are now routinely available from satellite InSAR and are used for flagging anomalous ground motion, but not yet forecasting. We test conventional time series forecasting methods such as SARIMA and supervised machine learning approaches such as long short‐term memory (LSTM) compared to simple function extrapolation. We focus initially on forecasting seasonal signals and begin by characterizing the time‐series using sinusoid fitting, seasonal decomposition, and autocorrelation functions. We find that the three measures are broadly comparable but identify different types of seasonal characteristic. We use this to select a set of 310 points with highly seasonal characteristics and test the three chosen forecasting methods over prediction windows of 1–9 months. The lowest overall median RMSE values are obtained for SARIMA when considering short term predictions (<1 month), whereas sinusoid extrapolation produces the lowest median RMSE values for longer predictions (>6 months). Machine learning methods (LSTM) perform less well. We then test the prediction methods on 2,000 randomly selected points with a range of seasonalities and find that simple extrapolation of a constant function performed better overall than any of the more sophisticated time series prediction methods. Comparisons between seasonality and RMSE show a small improvement in performance with increasing seasonality. This proof‐of‐concept study demonstrates the potential of time‐series prediction for InSAR data but also highlights the limitations of applying these techniques to nonperiodic signals or individual measurement points. We anticipate future developments, especially to shorter timescales, will have a broad range of potential applications, from infrastructure stability to volcanic eruptions.

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“…having no threat to infrastructure) make Hollin Hill an ideal site to trial new landslide monitoring techniques; the British Geological Survey has investigated and monitored landslides at Hollin Hill for several years (e.g. Chambers et al, 2008Chambers et al, , 2011Gunn et al, 2013;Merritt et al, 2013). Two of the lobes of slumped material were instrumented with three clusters of active waveguides (as described by Dixon et al (2014)); the instrumentation cluster locations are shown in Fig.…”

Section: The Hollin Hill Case Study Introductionmentioning

confidence: 99%

Acoustic emission monitoring of a soil slope: Comparisons with continuous deformation measurements

Smith

Dixon

Meldrum

et al. 2014

Géotechnique Letters

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Acoustic emission (AE) has become an established approach to monitor the stability of soil slopes. However, the challenge has been to develop strategies to interpret and quantify deformation behaviour from the measured AE. This paper presents the first comparison of continuous AE (measured using an active waveguide) and continuous subsurface deformation measurements. The active waveguide is installed in a borehole through a slope and comprises a metal waveguide rod or tube with a granular backfill surround. When the host slope deforms, the column of granular backfill also deforms, generating AE that can propagate along the waveguide. This paper presents results from a field trial at a reactivated soil slope in North Yorkshire, UK. The measurements confirm that AE rates generated are directly proportional to the velocity of slope movement (e.g. the AE rate versus velocity relationship determined for a series of slope movement events produced an R 2 value of 0?8) and demonstrate the performance of AE monitoring of active waveguides to provide continuous information on slope displacements and displacement rates with high temporal resolution.

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Geophysical Anatomy of the Hollin Hill Landslide, North Yorkshire, UK

Cited by 6 publications

References 4 publications

Moisture monitoring in clay embankments using electrical resistivity tomography

Moisture monitoring in clay embankments using electrical resistivity tomography

Time‐Series Prediction Approaches to Forecasting Deformation in Sentinel‐1 InSAR Data

Acoustic emission monitoring of a soil slope: Comparisons with continuous deformation measurements

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