The characterisation of the subsurface of a landslide is a critical step in developing ground models that inform planned mitigation measures, remediation works or future early-warning of instability. When a landslide failure may be imminent, the time pressures on producing such models may be great. Geoelectrical and seismic geophysical surveys are able to rapidly acquire volumetric data across large areas of the subsurface at the slope-scale. However, analysis of the individual model derived from each survey is typically undertaken in isolation, and a robust, accurate interpretation is highly dependent on the experience and skills of the operator. We demonstrate a machine learning process for constructing a rapid reconnaissance ground model, by integrating several sources of geophysical data in to a single ground model in a rapid and objective manner. Firstly, we use topographic data acquired by a UAV survey to co-locate three geophysical surveys of the Hollin Hill Landslide Observatory in the UK. The data are inverted using a joint 2D mesh, resulting in a set of co-located models of resistivity, P-wave velocity and S-wave velocity. Secondly, we analyse the relationships and trends present between the variables for each point in the mesh (resistivity, P-wave velocity, S-wave velocity, depth) to identify correlations. Thirdly, we use a Gaussian Mixture Model (GMM), a form of unsupervised machine learning, to classify the geophysical data into cluster groups with similar ranges and trends in measurements. The resulting model created from probabilistically assigning each subsurface point to a cluster group characterises the heterogeneity of landslide materials based on their geophysical properties, identifying the major subsurface discontinuities at the site. Finally, we compare the results of the cluster groups to intrusive borehole data, which show good agreement with the spatial variations in lithology. We demonstrate the applicability of integrated geophysical surveys coupled with simple unsupervised machine learning for producing rapid reconnaissance ground models in time-critical situations with minimal prior knowledge about the subsurface.
The movements of permanently installed monitoring electrodes on an active landslide will cause artefacts in the resulting resistivity images if their positions are not continuously updated and incorporated in the inversion. In this paper we investigate the effects of electrode movements on time-lapse resistivity tomography using a simple analytical model and real data. The correspondence between this model and the data is sufficiently good to be able to predict the electrode movements with reasonable accuracy. We show that the model can be used to invert the downslope displacements of the electrodes from their original baseline positions using only the time-lapse ratios of the apparent resistivity data. The example datasets are taken from an electrode array on an active lobe of a landslide. We show that the electrode positions can be recovered to an accuracy of 4% of the baseline electrode spacing, which is sufficient to correct the artefacts in the resistivity images. Using a time-lapse sequence of resistivity data, we demonstrate that this technique can be used to track the movement of the landslide over time to the same level of accuracy.
Summary Electrodes installed on active landslides and vulnerable earthworks to monitor changes in resistivity associated with moisture dynamics can be subject to movement. This affects the geoelectrical data and leads to errors in the resulting Electrical Resistivity Tomography (ERT) images. This paper demonstrates the selection of appropriate ERT measurements to provide sensitivity to electrode displacements in both directions on a plane. A combination of linear and equatorial dipole-dipole measurements are considered, which permit use on rectangular grids of any aspect ratio. A Gauss-Newton inversion scheme is developed that allows for the incorporation of constraints based on the magnitude and direction of movement. The effects of the constraints are demonstrated using synthetic data designed to simulate a planned laboratory experiment. In the presence of realistic levels of data noise and uncertainty in the preferred directions of movement, the inversion method is able to recover the displacements of the electrodes with a root-mean-square misfit of less than 1% of the electrode spacing.
<p>A full understanding of the moisture regimes in agricultural soils is critical when considering a climatic future that is potentially both drier and hotter. The study of near-surface hydrodynamics necessitates a high temporal frequency of measurements to allow the capture of fast-moving processes. In addition to this, heterogeneities in soils, coupled with spatially variable moisture contents, mean that point sensors alone can miss valuable information when studying soil hydrodynamics.</p><p>The sensitivity of Electrical Resistivity Tomography (ERT) to soil water content means that it is ideally suited to studying soil hydrodynamics. As a non-invasive technique, it is able to effectively capture spatial heterogeneities, while repeating ERT measurements at short time intervals enables the monitoring of rapid soil moisture changes.</p><p>In order to study Conservation Agriculture (CA) - an agricultural technique shown to have increased water and heat stress resilience - we collect frequent ERT measurements (&#8804; daily) over a long period of time (> 2 years) using PRIME - a low-cost, low-power, ERT monitoring instrument developed by the British Geological Survey. Our equipment is permanently installed at three agricultural observatories in southern Africa (Malawi, Zimbabwe, and Zambia), and is complemented by many co-located point sensors monitoring soil temperature, water content, and matric potential.</p><p>We compare the hydrodynamics - derived from ERT data - found under CA, with those under conventional agricultural tillage methods, to better understand the root of CA&#8217;s resilience to hot and dry weather conditions. We use laboratory measurements together with the high number of co-located point sensor measurements to build pedological / geophysical relationships for each site, comparing those derived from laboratory scale samples to those derived from in-field measurements.</p>
Abstract Moisture-induced landslides are a global geohazard; mitigating the risk posed by landslides requires an understanding of the hydrological and geological conditions present within a given slope. Recently, numerous geophysical studies have been attempted to characterise slow-moving landslides, with an emphasis on developing geoelectrical methods as a hydrological monitoring tool. However, landslides pose specific challenges for processing geoelectrical data in long-term monitoring contexts as the sensor arrays can move with slope movements. Here we present an approach for processing long-term (over 8 years) geoelectrical monitoring data from an active slow-moving landslide, Hollin Hill, situated in Lias rocks in the southern Howardian Hills, UK. These slope movements distorted the initial setup of the monitoring array and need to be incorporated into a time-lapse resistivity processing workflow to avoid imaging artefacts. We retrospectively sourced seven digital terrain models to inform the topography of our imaging volumes, which were acquired by either Unmanned Aerial Vehicle (UAV)-based photogrammetry or terrestrial laser ranging systems. An irregular grid of wooden pegs was periodically surveyed with a global position system, from which distortions to the terrain model and electrode positions can be modelled with thin plate splines. In order to effectively model the time-series electrical resistivity images, a baseline constraint is applied within the inversion scheme; the result of the study is a time-lapse series of resistivity volumes which also incorporate slope movements. The workflow presented here should be adaptable for other studies focussed on geophysical/geotechnical monitoring of unstable slopes.
In this work we describe a study where automated time-lapse electrical resistivity tomography (ALERT) monitoring technology has been installed on a section of Victorian embankment on the Great Central Railway (Nottingham, United Kingdom). Raw datasets collected by the ALERT system have been processed/filtered, and inverted to yield a 3D resistivity distribution which is temperature corrected and converted to gravimetric moisture content using a relationship established by laboratory testing. Electrical resistivity tomography monitoring has been used to characterize the internal structure of the embankment, and image moisture content changes and wetting front development at a high spatial resolution. Monitoring has been undertaken at the test site to determine seasonal temperature changes in the subsurface; these data have been used to correct for temperature effects. We fitted the resistivity data as a function of gravimetric moisture content by modifying the Waxman-Smits model. Using results from laboratory testing, a best fit is computed and used to establish a resistivity, gravimetric moisture content relationship, used to facilitate property translation from temperature corrected resistivity to gravimetric moisture content. These results indicate that ERT has potential to identify structures and processes related to instability at an early stage in their development.
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