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Many different approaches have been developed to regularise the time-lapse geoelectrical inverse problem. While their advantages and limitations have been demonstrated using synthetic models, there have been few direct comparisons of their performance using field data. We test four time-lapse inversion methods (independent inversion, temporal smoothness-constrained 4D inversion, spatial smoothness constrained inversion of temporal data differences, and sequential inversion with spatial smoothness constraints on the model and its temporal changes). We focus on the applicability of these methods to automated processing of geoelectrical monitoring data in near real-time. In particular, we examine windowed 4D inversion, the use of short sequences of time-lapse data, without which the 4D method would not be suitable in the near real-time context. We develop measures of internal consistency for the different methods so that the effects of the use of short time windows or the choice of baseline data set can be compared. The resulting inverse models are assessed against qualitative and quantitative ground truth information. Our findings are that 4D inversion of the full data set performed best, and that windowed 4D inversion retained the majority of its benefits while also being applicable to applications requiring near real-time inversion.

Many different approaches have been developed to regularise the time-lapse geoelectrical inverse problem. While their advantages and limitations have been demonstrated using synthetic models, there have been few direct comparisons of their performance using field data. We test four time-lapse inversion methods (independent inversion, temporal smoothness-constrained 4D inversion, spatial smoothness constrained inversion of temporal data differences, and sequential inversion with spatial smoothness constraints on the model and its temporal changes). We focus on the applicability of these methods to automated processing of geoelectrical monitoring data in near real-time. In particular, we examine windowed 4D inversion, the use of short sequences of time-lapse data, without which the 4D method would not be suitable in the near real-time context. We develop measures of internal consistency for the different methods so that the effects of the use of short time windows or the choice of baseline data set can be compared. The resulting inverse models are assessed against qualitative and quantitative ground truth information. Our findings are that 4D inversion of the full data set performed best, and that windowed 4D inversion retained the majority of its benefits while also being applicable to applications requiring near real-time inversion.

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 surv

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 surv

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 surv

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 surv

Seismic refraction tomography provides images of the elastic properties of subsurface materials in landslide settings. Seismic velocities are sensitive to changes in moisture content, which is a triggering factor in the initiation of many landslides. However, the application of the method to long-term monitoring of landslides is rarely used, given the challenges in undertaking repeat surveys and in handling and minimizing the errors arising from processing time-lapse surveys. This work presents a simple method and workflow for producing a reliable time-series of inverted seismic velocity models. This method is tested using data acquired during a recent, novel, long-term seismic refraction monitoring campaign at an active landslide in the UK. Potential sources of error include those arising from inaccurate and inconsistent determination of first-arrival times, inaccurate receiver positioning, and selection of inappropriate inversion starting models. At our site, a comparative analysis of variations in seismic velocity to real-world variations in topography over time shows that topographic error alone can account for changes in seismic velocity of greater than ±10% in a significant proportion (23%) of the data acquired. The seismic velocity variations arising from real material property changes at the near-surface of the landslide, linked to other sources of environmental data, are demonstrated to be of a similar magnitude. Over the monitoring period we observe subtle variations in the bulk seismic velocity of the sliding layer that are demonstrably related to variations in moisture content. This highlights the need to incorporate accurate topographic information for each time-step in the monitoring time-series. The goal of the proposed workflow is to minimize the sources of potential errors, and to preserve the changes observed by real variations in the subsurface. Following the workflow produces spatially comparable, time-lapse velocity cross-sections formulated fro

Seismic refraction tomography provides images of the elastic properties of subsurface materials in landslide settings. Seismic velocities are sensitive to changes in moisture content, which is a triggering factor in the initiation of many landslides. However, the application of the method to long-term monitoring of landslides is rarely used, given the challenges in undertaking repeat surveys and in handling and minimizing the errors arising from processing time-lapse surveys. This work presents a simple method and workflow for producing a reliable time-series of inverted seismic velocity models. This method is tested using data acquired during a recent, novel, long-term seismic refraction monitoring campaign at an active landslide in the UK. Potential sources of error include those arising from inaccurate and inconsistent determination of first-arrival times, inaccurate receiver positioning, and selection of inappropriate inversion starting models. At our site, a comparative analysis of variations in seismic velocity to real-world variations in topography over time shows that topographic error alone can account for changes in seismic velocity of greater than ±10% in a significant proportion (23%) of the data acquired. The seismic velocity variations arising from real material property changes at the near-surface of the landslide, linked to other sources of environmental data, are demonstrated to be of a similar magnitude. Over the monitoring period we observe subtle variations in the bulk seismic velocity of the sliding layer that are demonstrably related to variations in moisture content. This highlights the need to incorporate accurate topographic information for each time-step in the monitoring time-series. The goal of the proposed workflow is to minimize the sources of potential errors, and to preserve the changes observed by real variations in the subsurface. Following the workflow produces spatially comparable, time-lapse velocity cross-sections formulated fro

Geophysical monitoring of landslides can provide insights into spatial and temporal variations of subsurface properties associated with slope failure. Recent improvements in equipment, data analysis, and field operations have led to a significant increase in the use of such techniques in monitoring. Geophysical methods complement intrusive approaches, which sample only a very small proportion of the subsurface, and walk-over or remotely sensed data, which principally provide information only at the ground surface. In particular, recent studies show that advances in geophysical instrumentation, data processing, modeling, and interpretation in the context of landslide monitoring are significantly improving the characterization of hillslope hydrology and soil and rock hydrology and strength and their dynamics over time. This review appraises the state of the art of geophysical monitoring, as applied to moisture-induced landslides. Here we focus on technical and practical uses of time-lapse methods in geophysics applied to monitoring moisture-induced landslide. The case studies identified in this review show that several geophysical techniques are currently used in the monitoring of subsurface landslide processes. These geophysical contributions to monitoring and predicting the evolution of landslide processes are currently underrealized. Hence, the further integration of multiple-parametric and geotechnically coupled geophysical monitoring systems has considerable potential. The complementary nature of certain methods to map the distribution of subsurface moisture and elastic moduli will greatly increase the predictive and monitoring capacity of early warning systems in moisture-induced landslide settings.

Geophysical monitoring of landslides can provide insights into spatial and temporal variations of subsurface properties associated with slope failure. Recent improvements in equipment, data analysis, and field operations have led to a significant increase in the use of such techniques in monitoring. Geophysical methods complement intrusive approaches, which sample only a very small proportion of the subsurface, and walk-over or remotely sensed data, which principally provide information only at the ground surface. In particular, recent studies show that advances in geophysical instrumentation, data processing, modeling, and interpretation in the context of landslide monitoring are significantly improving the characterization of hillslope hydrology and soil and rock hydrology and strength and their dynamics over time. This review appraises the state of the art of geophysical monitoring, as applied to moisture-induced landslides. Here we focus on technical and practical uses of time-lapse methods in geophysics applied to monitoring moisture-induced landslide. The case studies identified in this review show that several geophysical techniques are currently used in the monitoring of subsurface landslide processes. These geophysical contributions to monitoring and predicting the evolution of landslide processes are currently underrealized. Hence, the further integration of multiple-parametric and geotechnically coupled geophysical monitoring systems has considerable potential. The complementary nature of certain methods to map the distribution of subsurface moisture and elastic moduli will greatly increase the predictive and monitoring capacity of early warning systems in moisture-induced landslide settings.

Flow-like landslides in clayey soils represent serious threats for populations and infrastructures and have been the subject of numerous studies in the past decade. However, despite the rising need for landslide mitigation with growing urbanization, the transient mechanisms involved in the solid-fluid transition are still poorly understood. One way of characterizing the solid-fluid transition is to carry out rheometrical tests on clayey soil samples to assess the evolution of viscosity with the shear stress. In this study, we carried out geotechnical and rheometrical tests on clayey samples collected from six flow-like landslides in order to assess if these clayey soils exhibit similar characteristics when they fluidize (solid-fluid transition). The results show that (1) all tested soils except one exhibit a yield-stress fluid behavior that can be associated with a bifurcation in viscosity (described by the critical shear rate γ c ˙ γc˙ ) and in shear modulus G; (2) the larger the amplitude of the viscosity bifurcation, the larger the associated drop in G; and (3) the water content (w) deviation from the Atterberg liquid limit (LL) seem a key parameter controlling a common mechanical behavior of these soils at the solid-fluid transition. We propose exponential laws describing the evolution of the critical shear stress τc, the critical shear rate γ c ˙ γc˙ , and the shear modulus G as a function of the deviation w-LL.

Electrical resistivity tomography (ERT) is increasingly being used to investigate unstable slopes and monitor the hydrogeological processes within. But movement of electrodes or incorrect placement of electrodes with

Resistivity monitoring surveys are used to detect temporal changes in the subsurface using repeated measurements over the same site. The positions of the electrodes are typically measured at the start of the survey program and possibly at occasional later times. In areas with unstable ground, such as landslide‐prone slopes, the positions of the electrodes can be displaced by ground movements. If this occurs at times when the positions of the electrodes are not directly measured, they have to be estimated. This can be done by interpolation or, as in recent developments, from the resistivity data using new inverse methods. The smoothness‐constrained least squares optimisation method can be modified to include the electrode positions as additional unknown parameters. The Jacobian matrices with the sensitivity of the apparent resistivity measurements to changes in the electrode positions are then required by the optimisation method. In this paper, a fast adjoint‐equation method is used to calculate the Jacobian matrices required by the least squares method to reduce the calculation time. In areas with large near‐surface resistivity contrasts, the inversion routine sometimes cannot accurately distinguish between electrode displacements and subsurface resistivity variations. To overcome this problem, the model for the initial time‐lapse dataset (with accurately known electrode positions) is used as the starting model for the inversion of the later‐time dataset. This greatly improves the accuracy of the estimated electrode positions compared to the use of a homogeneous half‐space starting model. In areas where the movement of the electrodes is expected to occur in a fixed direction, the method of transformations can be used to include this information as an additional constraint in the optimisation routine.

Effective management of railway infrastructure is becoming increasingly reliant upon remote condition monitoring of geotechnical asset condition. Current monitoring approaches focus on confirmation of the morphological effects caused by subsurface processes driving deterioration. However, geophysical imaging offers new opportunities for ‘predict and prevent’ practices, providing access to monitoring internal property change patterns preceding these morphological responses. Geophysical methods utilize disturbances that propagate through and holistically sample earthworks and are especially suited to imaging the unique heterogeneity of aged embankments. In this case study, surface wave seismic surveys are interpreted to construct a stiffness ground model consistent with a heterogeneous embankment comprising local borrow materials. Time-lapse electrical resistivity imaging was also used to investigate and visualise ground water ingress and movement within this ground model. Ground water movement was shown to be highly dynamic, responding very quickly to local storm events with infiltration into the embankment within hours. Subsequent wetting and drying cycles throughout the embankment’s lifespan have caused the dissolution, mobilisation and re-precipitation of soluble minerals within the fill materials. This process has driven the deterioration of the fill fabric, which is evidenced in thin sections by voids and localised rupture about in situ mineral growths. Finally, we provide a framework showing how geophysical methods could support more risk-based asset management practices of the future.

A common factor in landslide activation (or reactivation) is subsurface moisture and associated pore pressure variations linked to rainfall. Monitoring of these subsurface hydrogeological processes is necessary to improve our understanding of water-induced landslide activation. Geophysical approaches, electrical methods in particular, are increasingly being applied to landslide monitoring because they provide non-invasive spatial information in heterogeneous subsurface environments that can be difficult to characterise using surface observations or intrusive sampling alone. Electrical techniques are sensitive to changing subsurface moisture conditions, and have proven to be a useful tool for investigating the hydrogeology of natural and engineered slopes. The objectives of this investigation were to further develop electrical resistance monitoring for slope stability assessment, and to validate the approach at an intermittently-active UK landslide system to advance the understanding of complex landslide activation mechanisms. A long-term transfer resistance dataset was collected from a grid of electrodes to allow spatial monitoring of the landslide. These data were interpreted using a synthesis of rainfall, temperature, GPS and piezometric records. The resistance data were corrected for seasonal temperature variations and electrode movements were monitored, as these processes were shown to mask moisture related changes. Results reveal that resistance monitoring is sensitive to soil moisture accumulation, including changes in piezometric levels, and can be used to study the principal activation mechanism of slow-moving shallow earthflows. Spatial monitoring using resistance maps was shown to be particularly valuable as it revealed the evolution of subsurface moisture distribution, in the lead up to landslide activation. Key benefits of this approach are that it provides a simple, rapid and non-invasive means of spatially monitoring subsurface moisture dynamics linked

Effective management of railway infrastructure is becoming increasingly reliant upon remote condition monitoring of geotechnical asset condition. Current monitoring approaches focus on confirmation of the morphological effects caused by subsurface processes driving deterioration. However, geophysical imaging offers new opportunities for ‘predict and prevent’ practices, providing access to monitoring internal property change patterns preceding these morphological responses. Geophysical methods utilize disturbances that propagate through and holistically sample earthworks and are especially suited to imaging the unique heterogeneity of aged embankments. In this case study, surface wave seismic surveys are interpreted to construct a stiffness ground model consistent with a heterogeneous embankment comprising local borrow materials. Time-lapse electrical resistivity imaging was also used to investigate and visualise ground water ingress and movement within this ground model. Ground water movement was shown to be highly dynamic, responding very quickly to local storm events with infiltration into the embankment within hours. Subsequent wetting and drying cycles throughout the embankment’s lifespan have caused the dissolution, mobilisation and re-precipitation of soluble minerals within the fill materials. This process has driven the deterioration of the fill fabric, which is evidenced in thin sections by voids and localised rupture about in situ mineral growths. Finally, we provide a framework showing how geophysical methods could support more risk-based asset management practices of the future.

Resistivity monitoring surveys are used to detect temporal changes in the subsurface using repeated measurements over the same site. The positions of the electrodes are typically measured at the start of the survey program and possibly at occasional later times. In areas with unstable ground, such as landslide‐prone slopes, the positions of the electrodes can be displaced by ground movements. If this occurs at times when the positions of the electrodes are not directly measured, they have to be estimated. This can be done by interpolation or, as in recent developments, from the resistivity data using new inverse methods. The smoothness‐constrained least squares optimisation method can be modified to include the electrode positions as additional unknown parameters. The Jacobian matrices with the sensitivity of the apparent resistivity measurements to changes in the electrode positions are then required by the optimisation method. In this paper, a fast adjoint‐equation method is used to calculate the Jacobian matrices required by the least squares method to reduce the calculation time. In areas with large near‐surface resistivity contrasts, the inversion routine sometimes cannot accurately distinguish between electrode displacements and subsurface resistivity variations. To overcome this problem, the model for the initial time‐lapse dataset (with accurately known electrode positions) is used as the starting model for the inversion of the later‐time dataset. This greatly improves the accuracy of the estimated electrode positions compared to the use of a homogeneous half‐space starting model. In areas where the movement of the electrodes is expected to occur in a fixed direction, the method of transformations can be used to include this information as an additional constraint in the optimisation routine.

A common factor in landslide activation (or reactivation) is subsurface moisture and associated pore pressure variations linked to rainfall. Monitoring of these subsurface hydrogeological processes is necessary to improve our understanding of water-induced landslide activation. Geophysical approaches, electrical methods in particular, are increasingly being applied to landslide monitoring because they provide non-invasive spatial information in heterogeneous subsurface environments that can be difficult to characterise using surface observations or intrusive sampling alone. Electrical techniques are sensitive to changing subsurface moisture conditions, and have proven to be a useful tool for investigating the hydrogeology of natural and engineered slopes. The objectives of this investigation were to further develop electrical resistance monitoring for slope stability assessment, and to validate the approach at an intermittently-active UK landslide system to advance the understanding of complex landslide activation mechanisms. A long-term transfer resistance dataset was collected from a grid of electrodes to allow spatial monitoring of the landslide. These data were interpreted using a synthesis of rainfall, temperature, GPS and piezometric records. The resistance data were corrected for seasonal temperature variations and electrode movements were monitored, as these processes were shown to mask moisture related changes. Results reveal that resistance monitoring is sensitive to soil moisture accumulation, including changes in piezometric levels, and can be used to study the principal activation mechanism of slow-moving shallow earthflows. Spatial monitoring using resistance maps was shown to be particularly valuable as it revealed the evolution of subsurface moisture distribution, in the lead up to landslide activation. Key benefits of this approach are that it provides a simple, rapid and non-invasive means of spatially monitoring subsurface moisture dynamics linked

Interactions between groundwater (GW) and surface water (SW) have important implications for water quantity, water quality, and ecological health. The subsurface region proximal to SW bodies, the GW–SW interface, is crucial as it actively regulates the transfer of nutrients, contaminants, and water between GW systems and SW environments. However, geological, hydrological, and biogeochemical heterogeneity in the GW–SW interface makes it difficult to characterise with direct observations. Over the past two decades geophysics has been increasingly used to characterise spatial and temporal variability throughout the GW–SW interface. Geophysics is a powerful tool in evaluating structural heterogeneity, revealing zones of GW discharge, and monitoring hydrological processes. Geophysics should be used alongside traditional hydrological and biogeochemical methods to provide additional information about the subsurface. Further integration of commonly used geophysical techniques, and adoption of emerging techniques, has the potential to improve understanding of the properties and processes of the GW–SW interface, and ultimately the implications for water quality and environmental health.

Interactions between groundwater (GW) and surface water (SW) have important implications for water quantity, water quality, and ecological health. The subsurface region proximal to SW bodies, the GW-SW interface, is crucial as it actively regulates the transfer of nutrients, contaminants, and water between GW systems and SW environments. However, geological, hydrological, and biogeochemical heterogeneity in the GW-SW interface makes it difficult to characterise with direct observations. Over the past two decades geophysics has been increasingly used to characterise spatial and temporal variability throughout the GW-SW interface. Geophysics is a powerful tool in evaluating structural heterogeneity, revealing zones of GW discharge, and monitoring hydrological processes. Geophysics should be used alongside traditional hydrological and biogeochemical methods to provide additional information about the subsurface. Further integration of commonly used geophysical techniques, and adoption of emerging techniques, has the potential to improve understanding of the properties and processes of the GW-SW interface, and ultimately the implications for water quality and environmental health.

Interactions between groundwater (GW) and surface water (SW) have important implications for water quantity, water quality, and ecological health. The subsurface region proximal to SW bodies, the GW-SW interface, is crucial as it actively regulates the transfer of nutrients, contaminants, and water between GW systems and SW environments. However, geological, hydrological, and biogeochemical heterogeneity in the GW-SW interface makes it difficult to characterise with direct observations. Over the past two decades geophysics has been increasingly used to characterise spatial and temporal variability throughout the GW-SW interface. Geophysics is a powerful tool in evaluating structural heterogeneity, revealing zones of GW discharge, and monitoring hydrological processes. Geophysics should be used alongside traditional hydrological and biogeochemical methods to provide additional information about the subsurface. Further integration of commonly used geophysical techniques, and adoption of emerging techniques, has the potential to improve understanding of the properties and processes of the GW-SW interface, and ultimately the implications for water quality and environmental health.

Interactions between groundwater (GW) and surface water (SW) have important implications for water quantity, water quality, and ecological health. The subsurface region proximal to SW bodies, the GW–SW interface, is crucial as it actively regulates the transfer of nutrients, contaminants, and water between GW systems and SW environments. However, geological, hydrological, and biogeochemical heterogeneity in the GW–SW interface makes it difficult to characterise with direct observations. Over the past two decades geophysics has been increasingly used to characterise spatial and temporal variability throughout the GW–SW interface. Geophysics is a powerful tool in evaluating structural heterogeneity, revealing zones of GW discharge, and monitoring hydrological processes. Geophysics should be used alongside traditional hydrological and biogeochemical methods to provide additional information about the subsurface. Further integration of commonly used geophysical techniques, and adoption of emerging techniques, has the potential to improve understanding of the properties and processes of the GW–SW interface, and ultimately the implications for water quality and environmental health.

Landslides are hazardous events with often disastrous consequences. Monitoring landslides with observations of high spatio-temporal resolution can help mitigate such hazards. Mini unmanned aerial vehicles (UAVs) complemented by structure-from-motion (SfM) photogrammetry and modern per-pixel image matching algorithms can deliver a time-series of landslide elevation models in an automated and inexpensive way. This research investigates the potential of a mini UAV, equipped with a Panasonic Lumix DMC-LX5 compact camera, to provide surface deformations at acceptable levels of accuracy for landslide assessment. The study adopts a self-calibrating bundle adjustment-SfM pipeline using ground control points (GCPs). It evaluates misalignment biases and unresolved systematic errors that are transferred through the SfM process into the derived elevation models. To cross-validate the research outputs, results are compared to benchmark observations obtained by standard surveying techniques. The data is collected with 6 cm ground sample distance (GSD) and is shown to achieve planimetric and vertical accuracy of a few centimetres at independent check points (ICPs). The co-registration error of the generated elevation models is also examined in areas of stable terrain. Through this error assessment, the study estimates that the vertical sensitivity to real terrain change of the tested landslide is equal to 9 cm.

A methodology for developing resistivity-moisture content relationships of materials associated with a clayey landslide is presented. Key elements of the methodology include sample selection and preparation, laboratory measurement of resistivity with changing moisture content, and the derivation of models describing the relationship between resistivity and moisture content. Laboratory resistivity measurements show that the techniques utilised (samples and square array) have considerable potential as a means of electropetrophysical calibration of engineering soils and weak rock. Experimental electrical resistivity results show a hierarchy of values dependent on sample lithology, with silty clay exhibiting the lowest resistivities, followed by siltstones and sands, which return the highest resistivities. In addition, finer grained samples show a greater degree of anisotropy between measurement orientations than coarser grained samples. However, suitability of results in light of issues such as sample cracking and electrical conduction must be identified and accounted for if the results are to be accurately up-scaled to inverted model resistivity results. The existence of directional anisotropy makes model calibration curve selection more difficult due to variability in the range of measured laboratory resistances. The use of larger measurement array size means that experimental data will be more representative of bulk lithological properties. In addition, use of electrodes with a relatively high surface area (wide diameter) help maintain low contact resistances and repeat measurement error, relative to narrow electrodes. Variation exists between the fit of experimental data and petrophysical models. Model fit is best for clay-dominated samples but fits less well for sand-dominated samples. Waxman–Smits equation is appropriately applied in this investigation as all samples have considerable clay mineral content, as is shown in non-negligible CEC results. The incorporat

A new framework for automatically tracking subsurface tracers in electrical resistivity tomography (ERT) monitoring images is presented. Using computer vision and Bayesian inference techniques, in the form of a Kalman filter, the trajectory of a subsurface tracer is monitored by predicting and updating a state model representing its movements. Observations for the Kalman filter are gathered using the maximally stable volumes algorithm, which is used to dynamically threshold local regions of an ERT image sequence to detect the tracer at each time step. The application of the framework to the results of 2-D and 3-D tracer monitoring experiments show that the proposed method is effective for detecting and tracking tracer plumes in ERT images in the presence of noise, without intermediate manual intervention.

Wetlands provide crucial habitats, are critical in the global carbon cycle, and act as key biogeochemical and hydrological buffers. The effectiveness of these services is mainly controlled by hydrological processes, which can be highly variable both spatially and temporally due to structural complexity and seasonality. Spatial analysis of 2D geoelectrical monitoring data integrated into the interpretation of conventional hydrological data has been implemented to provide a detailed understanding of hydrological processes in a riparian wetland. A two-layered hydrological system was observed in the peat. In the lower part of the peat, upwelling of deeper groundwater from underlying deposits was considered the driver for a 30% increase in peat resistivity during Winter/Spring. In Spring/Summer there was a 60% decrease in resistivity in the near-surface peats due to plant transpiration and/or microbial activity. Water exchange between the layers only appeared to be initiated following large drops in the encircling surface water stage. For the first time, we showed that automated interpretation of geoelectrical data can be used to quantify shrink-swell of expandable soils, affecting hydrological parameters, such as, porosity and permeability. This study shows that an integrated interpretation of hydrological and geophysical data can significantly improve the understanding of wetland hydrological processes. Potentially, this approach can provide the basis for the evaluation of ecosystem services and may aid in the optimization of wetland management strategies.

Landslides are hazardous events with often disastrous consequences. Monitoring landslides with observations of high spatio-temporal resolution can help mitigate such hazards. Mini unmanned aerial vehicles (UAVs) complemented by structure-from-motion (SfM) photogrammetry and modern per-pixel image matching algorithms can deliver a time-series of landslide elevation models in an automated and inexpensive way. This research investigates the potential of a mini UAV, equipped with a Panasonic Lumix DMC-LX5 compact camera, to provide surface deformations at acceptable levels of accuracy for landslide assessment. The study adopts a self-calibrating bundle adjustment-SfM pipeline using ground control points (GCPs). It evaluates misalignment biases and unresolved systematic errors that are transferred through the SfM process into the derived elevation models. To cross-validate the research outputs, results are compared to benchmark observations obtained by standard surveying techniques. The data is collected with 6 cm ground sample distance (GSD) and is shown to achieve planimetric and vertical accuracy of a few centimetres at independent check points (ICPs). The co-registration error of the generated elevation models is also examined in areas of stable terrain. Through this error assessment, the study estimates that the vertical sensitivity to real terrain change of the tested landslide is equal to 9 cm.

A new framework for automatically tracking subsurface tracers in electrical resistivity tomography (ERT) monitoring images is presented. Using computer vision and Bayesian inference techniques, in the form of a Kalman filter, the trajectory of a subsurface tracer is monitored by predicting and updating a state model representing its movements. Observations for the Kalman filter are gathered using the maximally stable volumes algorithm, which is used to dynamically threshold local regions of an ERT image sequence to detect the tracer at each time step. The application of the framework to the results of 2-D and 3-D tracer monitoring experiments show that the proposed method is effective for detecting and tracking tracer plumes in ERT images in the presence of noise, without intermediate manual intervention.

Wetlands provide crucial habitats, are critical in the global carbon cycle, and act as key biogeochemical and hydrological buffers. The effectiveness of these services is mainly controlled by hydrological processes, which can be highly variable both spatially and temporally due to structural complexity and seasonality. Spatial analysis of 2D geoelectrical monitoring data integrated into the interpretation of conventional hydrological data has been implemented to provide a detailed understanding of hydrological processes in a riparian wetland. A two-layered hydrological system was observed in the peat. In the lower part of the peat, upwelling of deeper groundwater from underlying deposits was considered the driver for a 30% increase in peat resistivity during Winter/Spring. In Spring/Summer there was a 60% decrease in resistivity in the near-surface peats due to plant transpiration and/or microbial activity. Water exchange between the layers only appeared to be initiated following large drops in the encircling surface water stage. For the first time, we showed that automated interpretation of geoelectrical data can be used to quantify shrink-swell of expandable soils, affecting hydrological parameters, such as, porosity and permeability. This study shows that an integrated interpretation of hydrological and geophysical data can significantly improve the understanding of wetland hydrological processes. Potentially, this approach can provide the basis for the evaluation of ecosystem services and may aid in the optimization of wetland management strategies.

A methodology for developing resistivity-moisture content relationships of materials associated with a clayey landslide is presented. Key elements of the methodology include sample selection and preparation, laboratory measurement of resistivity with changing moisture content, and the derivation of models describing the relationship between resistivity and moisture content. Laboratory resistivity measurements show that the techniques utilised (samples and square array) have considerable potential as a means of electropetrophysical calibration of engineering soils and weak rock. Experimental electrical resistivity results show a hierarchy of values dependent on sample lithology, with silty clay exhibiting the lowest resistivities, followed by siltstones and sands, which return the highest resistivities. In addition, finer grained samples show a greater degree of anisotropy between measurement orientations than coarser grained samples. However, suitability of results in light of issues such as sample cracking and electrical conduction must be identified and accounted for if the results are to be accurately up-scaled to inverted model resistivity results. The existence of directional anisotropy makes model calibration curve selection more difficult due to variability in the range of measured laboratory resistances. The use of larger measurement array size means that experimental data will be more representative of bulk lithological properties. In addition, use of electrodes with a relatively high surface area (wide diameter) help maintain low contact resistances and repeat measurement error, relative to narrow electrodes. Variation exists between the fit of experimental data and petrophysical models. Model fit is best for clay-dominated samples but fits less well for sand-dominated samples. Waxman–Smits equation is appropriately applied in this investigation as all samples have considerable clay mineral content, as is shown in non-negligible CEC results. The incorporat

Dewatering systems used for mining and quarrying operations often result in highly artificial and complex groundwater conditions, which can be difficult to characterise and monitor using borehole point sampling approaches. Here automated time-lapse electrical resistivity tomography (ALERT) is considered as a means of monitoring subsurface groundwater dynamics associated with changes in the dewatering regime in an operational sand and gravel quarry. We considered two scenarios: the first was unplanned interruption to dewatering due to a pump failure for a period of several days, which involved comparing ALERT monitoring results before and after groundwater rebound; the second involved a planned interruption to pumping over a period of 6 h, for which near-continuous ALERT monitoring of groundwater rebound and drawdown was undertaken. The results of the second test were analysed using distribution guided clustering (DGC) to provide a more quantitative and objective assessment of changes in the subsurface over time. ALERT successfully identified groundwater level changes during both monitoring scenarios. It provided a more useful indication of the rate of water level rise and maximum water levels than piezometer monitoring results. This was due to the piezometers rapidly responding to pressure changes at depth, whilst ALERT/DGC provided information of slower changes associated with the storage and delayed drainage of water within the sediment. By applying DGC we were able to automatically and quantitatively define changes in the resistivity sections, which correlated well with the direct observations of groundwater at site. For ERT monitoring applications that generate numerous time series, the use of DGC could significantly enhance the efficiency of data interpretation, and provide a means of automating groundwater monitoring through assigning alarm thresholds associated with rapid changes in groundwater conditions.

Previous studies show that optimized arrays generated using the ‘Compare R’ method have significantly better resolution than conventional arrays. This method determines the optimum set of arrays by selecting those that give the maximum model resolution. The number of possible arrays (the comprehensive data set) increases with the fourth power of the number of electrodes. The optimization method faces practical limitations for 2-D survey lines with more than 60 electrodes where the number of possible arrays exceeds a million. Several techniques are proposed to reduce the calculation time for such survey lines. A single-precision version of the ‘Compare R’ algorithm using a new ranking function reduces the calculation time by two to eight times while providing results similar to the double-precision version. Recent improvements in computer GPU technology can reduce the calculation time by about seven times. The calculation time is reduced by half by using the fact that arrays that are symmetrical about the center of the line produce identical changes in the model resolution values. It is further reduced by more than thirty times by calculating the Sherman–Morrison update for all the possible two-electrode combinations, which are then used to calculate the model resolution values for the four-electrode arrays. The calculation time is reduced by more then ten times by using a subset of the comprehensive data set consisting of only symmetrical arrays. Tests with a synthetic model and field data set show that optimized arrays derived from this subset produce inversion models with differences of less than 10% from those derived using the full comprehensive data set. The optimized data sets produced models that are more accurate than the Wenner–Schlumberger array data sets in all the tests.

Systems and methods are described for monitoring temporal and spatial moisture content changes in clay embankments using electrical resistivity tomography (ERT) imaging. The methodology is based upon development of a robust relationship between fill resistivity and moisture content and its use in the transformation of resistivity image differences in terms of relative moisture content changes. Moisture level and moisture content movement applications are exemplified using two case histories from the UK. The first is the BIONICS embankment, near Newcastle (NE England), which was constructed in 2005 using varying degrees of compaction of a medium plasticity sandy, silty clay derived from the Durham Till. The second is a Victorian embankment south of Nottingham (Central England), constructed in 1897 using end tipping of Late Triassic siltstone and mudstone taken from local cuttings. Climate change forecasts for the UK suggest that transportation earthworks will be subjected to more sustained, higher temperatures and increased intensity of rainfall. Within the context of preventative geotechnical asset maintenance, ERT imaging can provide a monitoring framework to manage moisture movement and identify failure trigger conditions within embankments, thus supporting on demand inspection scheduling and low cost early interventions.

Many parts of the UK’s rail network were constructed in the mid‐19th century long before the advent of modern construction standards. Historic levels of low investment, poor maintenance strategies and the deleterious effects of climate change have resulted in critical elements of the rail network being at significant risk of failure. The majority of failures which have occurred over recent years have been triggered by extreme weather events. Advance assessment and remediation of earthworks is, however, significantly less costly than dealing with failures reactively. It is therefore crucial that appropriate approaches for assessment of the stability of earthworks are developed, so that repair work can be better targeted and failures avoided wherever possible. This extended abstract briefly discusses some preliminary results from an ongoing geophysical research project being carried out in order to study the impact of climate or seasonal weather variations on the stability of a century old railway embankment on the Gloucestershire Warwickshire steam railway line in Southern England.

Many parts of the UK’s rail network were constructed in the mid‐19th century long before the advent of modern construction standards. Historic levels of low investment, poor maintenance strategies and the deleterious effects of climate change have resulted in critical elements of the rail network being at significant risk of failure. The majority of failures which have occurred over recent years have been triggered by extreme weather events. Advance assessment and remediation of earthworks is, however, significantly less costly than dealing with failures reactively. It is therefore crucial that appropriate approaches for assessment of the stability of earthworks are developed, so that repair work can be better targeted and failures avoided wherever possible. This extended abstract briefly discusses some preliminary results from an ongoing geophysical research project being carried out in order to study the impact of climate or seasonal weather variations on the stability of a century old railway embankment on the Gloucestershire Warwickshire steam railway line in Southern England.

Dewatering systems used for mining and quarrying operations often result in highly artificial and complex groundwater conditions, which can be difficult to characterise and monitor using borehole point sampling approaches. Here automated time-lapse electrical resistivity tomography (ALERT) is considered as a means of monitoring subsurface groundwater dynamics associated with changes in the dewatering regime in an operational sand and gravel quarry. We considered two scenarios: the first was unplanned interruption to dewatering due to a pump failure for a period of several days, which involved comparing ALERT monitoring results before and after groundwater rebound; the second involved a planned interruption to pumping over a period of 6 h, for which near-continuous ALERT monitoring of groundwater rebound and drawdown was undertaken. The results of the second test were analysed using distribution guided clustering (DGC) to provide a more quantitative and objective assessment of changes in the subsurface over time. ALERT successfully identified groundwater level changes during both monitoring scenarios. It provided a more useful indication of the rate of water level rise and maximum water levels than piezometer monitoring results. This was due to the piezometers rapidly responding to pressure changes at depth, whilst ALERT/DGC provided information of slower changes associated with the storage and delayed drainage of water within the sediment. By applying DGC we were able to automatically and quantitatively define changes in the resistivity sections, which correlated well with the direct observations of groundwater at site. For ERT monitoring applications that generate numerous time series, the use of DGC could significantly enhance the efficiency of data interpretation, and provide a means of automating groundwater monitoring through assigning alarm thresholds associated with rapid changes in groundwater conditions.

Previous studies show that optimized arrays generated using the ‘Compare R’ method have significantly better resolution than conventional arrays. This method determines the optimum set of arrays by selecting those that give the maximum model resolution. The number of possible arrays (the comprehensive data set) increases with the fourth power of the number of electrodes. The optimization method faces practical limitations for 2-D survey lines with more than 60 electrodes where the number of possible arrays exceeds a million. Several techniques are proposed to reduce the calculation time for such survey lines. A single-precision version of the ‘Compare R’ algorithm using a new ranking function reduces the calculation time by two to eight times while providing results similar to the double-precision version. Recent improvements in computer GPU technology can reduce the calculation time by about seven times. The calculation time is reduced by half by using the fact that arrays that are symmetrical about the center of the line produce identical changes in the model resolution values. It is further reduced by more than thirty times by calculating the Sherman–Morrison update for all the possible two-electrode combinations, which are then used to calculate the model resolution values for the four-electrode arrays. The calculation time is reduced by more then ten times by using a subset of the comprehensive data set consisting of only symmetrical arrays. Tests with a synthetic model and field data set show that optimized arrays derived from this subset produce inversion models with differences of less than 10% from those derived using the full comprehensive data set. The optimized data sets produced models that are more accurate than the Wenner–Schlumberger array data sets in all the tests.

Systems and methods are described for monitoring temporal and spatial moisture content changes in clay embankments using electrical resistivity tomography (ERT) imaging. The methodology is based upon development of a robust relationship between fill resistivity and moisture content and its use in the transformation of resistivity image differences in terms of relative moisture content changes. Moisture level and moisture content movement applications are exemplified using two case histories from the UK. The first is the BIONICS embankment, near Newcastle (NE England), which was constructed in 2005 using varying degrees of compaction of a medium plasticity sandy, silty clay derived from the Durham Till. The second is a Victorian embankment south of Nottingham (Central England), constructed in 1897 using end tipping of Late Triassic siltstone and mudstone taken from local cuttings. Climate change forecasts for the UK suggest that transportation earthworks will be subjected to more sustained, higher temperatures and increased intensity of rainfall. Within the context of preventative geotechnical asset maintenance, ERT imaging can provide a monitoring framework to manage moisture movement and identify failure trigger conditions within embankments, thus supporting on demand inspection scheduling and low cost early interventions.

A ground model of an active and complex landslide system in instability prone Lias mudrocks of North Yorkshire, UK is developed through an integrated approach, utilising geophysical, geotechnical and remote sensing investigative methods. Surface geomorphology is mapped and interpreted using immersive 3D visualisation software to interpret airborne light detection and ranging data and aerial photographs. Subsurface structure is determined by core logging and 3D electrical resistivity tomography (ERT), which is deployed at two scales of resolution to provide a means of volumetrically characterising the subsurface expression of both site scale (tens of metres) geological structure, and finer (metre to sub-metre) scale earth-flow related structures. Petrophysical analysis of the borehole core samples is used to develop relationships between the electrical and physical formation properties, to aid calibration and interpretation of 3D ERT images. Results of the landslide investigation reveal that an integrated approach centred on volumetric geophysical imaging successfully achieves a detailed understanding of structure and lithology of a complex landslide system, which cannot be achieved through the use of remotely sensed data or discrete intrusive sampling alone.

For groundwater-surface water interactions to be understood in complex wetland settings, the architecture of the underlying deposits requires investigation at a spatial resolution sufficient to characterize significant hydraulic pathways. Discrete intrusive sampling using conventional approaches provides insufficient sample density and can be difficult to deploy on soft ground. Here a noninvasive geophysical imaging approach combining three-dimensional electrical resistivity tomography (ERT) and the novel application of gradient and isosurface-based edge detectors is considered as a means of illuminating wetland deposit architecture. The performance of three edge detectors were compared and evaluated against ground truth data, using a lowland riparian wetland demonstration site. Isosurface-based methods correlated well with intrusive data and were useful for defining the geometries of key geological interfaces (i.e., peat/gravels and gravels/Chalk). The use of gradient detectors approach was unsuccessful, indicating that the assumption that the steepest resistivity gradient coincides with the associated geological interface can be incorrect. These findings are relevant to the application of this approach in settings with a broadly layered geology with strata of contrasting resistivities. In addition, ERT revealed substantial structures in the gravels related to the depositional environment (i.e., braided fluvial system) and a complex distribution of low-permeability putty Chalk at the bedrock surface—with implications for preferential flow and variable exchange between river and groundwater systems. These results demonstrate that a combined approach using ERT and edge detectors can provide valuable information to support targeted monitoring and inform hydrological modeling of wetlands.

The internal moisture dynamics of an aged (> 100 years old) railway earthwork embankment, which is still in use, are investigated using 2D and 3D resistivity monitoring. A methodology was employed that included automated 3D ERT data capture and telemetric transfer with on-site power generation, the correction of resistivity models for seasonal temperature changes and the translation of subsurface resistivity distributions into moisture content based on petrophysical relationships developed for the embankment material. Visualization of the data as 2D sections, 3D tomograms and time series plots for different zones of the embankment enabled the development of seasonal wetting fronts within the embankment to be monitored at a high-spatial resolution and the respective distributions of moisture in the flanks, crest and toes of the embankment to be assessed. Although the embankment considered here is at no immediate risk of failure, the approach developed for this study is equally applicable to other more high-risk earthworks and natural slopes.

The River Lambourn and the associated wetlands comprise some of the least impacted chalk river systems in Britain. The associated lowland wetlands, due to their hydrological characteristics, may form a key conduit for, or barrier to, aqueous fluxes between land, rivers and groundwater. At Boxford, Berkshire, UK, a research site has been monitored for a number of years to establish the degree of groundwater and surface water interaction. Recently, two geoelectric monitoring lines have been added and measurements have been repeated every month.

Recent improvements in the capabilities of electrical resistivity tomography (ERT) in ground imaging have resulted in an increased application of this technique to the characterisation of landslides. Time-lapse ERT offers the possibility to image changes in the resistivity distribution over time, which may indicate hydrological processes triggering landslide movement. However, these measurements depend on knowing the exact locations of the electrodes, which, especially on landslides, are changing over time.

A novel method for the effective identification of bedrock subsurface elevation from electrical resistivity tomography images is described. Identifying subsurface boundaries in the topographic data can be difficult due to smoothness constraints used in inversion, so a statistical population-based approach is used that extends previous work in calculating isoresistivity surfaces. The analysis framework involves a procedure for guiding a clustering approach based on the fuzzy c-means algorithm. An approximation of resistivity distributions, found using kernel density estimation, was utilized as a means of guiding the cluster centroids used to classify data. A fuzzy method was chosen over hard clustering due to uncertainty in hard edges in the topography data, and a measure of clustering uncertainty was identified based on the reciprocal of cluster membership. The algorithm was validated using a direct comparison of known observed bedrock depths at two 3-D survey sites, using real-time GPS information of exposed bedrock by quarrying on one site, and borehole logs at the other. Results show similarly accurate detection as a leading isosurface estimation method, and the proposed algorithm requires significantly less user input and prior site knowledge. Furthermore, the method is effectively dimension-independent and will scale to data of increased spatial dimensions without a significant effect on the runtime. A discussion on the results by automated versus supervised analysis is also presented.

3-D electrical resistivity surveys and inversion models are required to accurately resolve structures in areas with very complex geology where 2-D models might suffer from artefacts. Many 3-D surveys use a grid where the number of electrodes along one direction (x) is much greater than in the perpendicular direction (y). Frequently, due to limitations in the number of independent electrodes in the multi-electrode system, the surveys use a roll-along system with a small number of parallel survey lines aligned along the x-direction. The ‘Compare R’ array optimization method previously used for 2-D surveys is adapted for such 3-D surveys. Offset versions of the inline arrays used in 2-D surveys are included in the number of possible arrays (the comprehensive data set) to improve the sensitivity to structures in between the lines. The array geometric factor and its relative error are used to filter out potentially unstable arrays in the construction of the comprehensive data set. Comparisons of the conventional (consisting of dipole-dipole and Wenner–Schlumberger arrays) and optimized arrays are made using a synthetic model and experimental measurements in a tank. The tests show that structures located between the lines are better resolved with the optimized arrays. The optimized arrays also have significantly better depth resolution compared to the conventional arrays.

3-D electrical resistivity surveys and inversion models are required to accurately resolve structures in areas with very complex geology where 2-D models might suffer from artefacts. Many 3-D surveys use a grid where the number of electrodes along one direction (x) is much greater than in the perpendicular direction (y). Frequently, due to limitations in the number of independent electrodes in the multi-electrode system, the surveys use a roll-along system with a small number of parallel survey lines aligned along the x-direction. The ‘Compare R’ array optimization method previously used for 2-D surveys is adapted for such 3-D surveys. Offset versions of the inline arrays used in 2-D surveys are included in the number of possible arrays (the comprehensive data set) to improve the sensitivity to structures in between the lines. The array geometric factor and its relative error are used to filter out potentially unstable arrays in the construction of the comprehensive data set. Comparisons of the conventional (consisting of dipole-dipole and Wenner–Schlumberger arrays) and optimized arrays are made using a synthetic model and experimental measurements in a tank. The tests show that structures located between the lines are better resolved with the optimized arrays. The optimized arrays also have significantly better depth resolution compared to the conventional arrays.

A ground model of an active and complex landslide system in instability prone Lias mudrocks of North Yorkshire, UK is developed through an integrated approach, utilising geophysical, geotechnical and remote sensing investigative methods. Surface geomorphology is mapped and interpreted using immersive 3D visualisation software to interpret airborne light detection and ranging data and aerial photographs. Subsurface structure is determined by core logging and 3D electrical resistivity tomography (ERT), which is deployed at two scales of resolution to provide a means of volumetrically characterising the subsurface expression of both site scale (tens of metres) geological structure, and finer (metre to sub-metre) scale earth-flow related structures. Petrophysical analysis of the borehole core samples is used to develop relationships between the electrical and physical formation properties, to aid calibration and interpretation of 3D ERT images. Results of the landslide investigation reveal that an integrated approach centred on volumetric geophysical imaging successfully achieves a detailed understanding of structure and lithology of a complex landslide system, which cannot be achieved through the use of remotely sensed data or discrete intrusive sampling alone.

For groundwater-surface water interactions to be understood in complex wetland settings, the architecture of the underlying deposits requires investigation at a spatial resolution sufficient to characterize significant hydraulic pathways. Discrete intrusive sampling using conventional approaches provides insufficient sample density and can be difficult to deploy on soft ground. Here a noninvasive geophysical imaging approach combining three-dimensional electrical resistivity tomography (ERT) and the novel application of gradient and isosurface-based edge detectors is considered as a means of illuminating wetland deposit architecture. The performance of three edge detectors were compared and evaluated against ground truth data, using a lowland riparian wetland demonstration site. Isosurface-based methods correlated well with intrusive data and were useful for defining the geometries of key geological interfaces (i.e., peat/gravels and gravels/Chalk). The use of gradient detectors approach was unsuccessful, indicating that the assumption that the steepest resistivity gradient coincides with the associated geological interface can be incorrect. These findings are relevant to the application of this approach in settings with a broadly layered geology with strata of contrasting resistivities. In addition, ERT revealed substantial structures in the gravels related to the depositional environment (i.e., braided fluvial system) and a complex distribution of low-permeability putty Chalk at the bedrock surface—with implications for preferential flow and variable exchange between river and groundwater systems. These results demonstrate that a combined approach using ERT and edge detectors can provide valuable information to support targeted monitoring and inform hydrological modeling of wetlands.

The internal moisture dynamics of an aged (> 100 years old) railway earthwork embankment, which is still in use, are investigated using 2D and 3D resistivity monitoring. A methodology was employed that included automated 3D ERT data capture and telemetric transfer with on-site power generation, the correction of resistivity models for seasonal temperature changes and the translation of subsurface resistivity distributions into moisture content based on petrophysical relationships developed for the embankment material. Visualization of the data as 2D sections, 3D tomograms and time series plots for different zones of the embankment enabled the development of seasonal wetting fronts within the embankment to be monitored at a high-spatial resolution and the respective distributions of moisture in the flanks, crest and toes of the embankment to be assessed. Although the embankment considered here is at no immediate risk of failure, the approach developed for this study is equally applicable to other more high-risk earthworks and natural slopes.