Landslide characterization using P- and S-wave seismic refraction tomography — The importance of elastic moduli

Uhlemann, S.; Hagedorn, S.; Dashwood, Ben; Maurer, Hansruedi; Gunn, David; Dijkstra, Tom; Chambers, Jonathan

doi:10.1016/j.jappgeo.2016.08.014

Cited by 78 publications

(61 citation statements)

References 64 publications

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“…This was calculated based on the data between the 10th and 90th percentiles in order to remove data outliers. This approach has previously been shown to provide robust inversion results, especially when applied to long‐term data where error characteristics may change over time and frequently applied error models tend to underestimate data errors [ Uhlemann et al ., ]. A forward modeling error of 2% of the measured resistance was added to the reciprocal error.…”

Section: Geoelectrical Monitoring Methodologymentioning

confidence: 99%

Four‐dimensional imaging of moisture dynamics during landslide reactivation

et al. 2017

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Landslides pose significant risks to communities and infrastructure, and mitigating these risks relies on understanding landslide causes and triggering processes. It has been shown that geophysical surveys can significantly contribute to the characterization of unstable slopes. However, hydrological processes can be temporally and spatially heterogeneous, requiring their related properties to be monitored over time. Geoelectrical monitoring can provide temporal and volumetric distributions of electrical resistivity, which are directly related to moisture content. To date, studies demonstrating this capability have been restricted to 2‐D sections, which are insufficient to capture the full degree of spatial heterogeneity. This study is the first to employ 4‐D (i.e., 3‐D time lapse) resistivity imaging on an active landslide, providing long‐term data (3 years) highlighting the evolution of moisture content prior to landslide reactivation and showing its decline post reactivation. Crucially, the time‐lapse inversion methodology employed here incorporates movements of the electrodes on the unstable surface. Although seasonal characteristics dominate the shallow moisture dynamics during the first 2 years with surficial drying in summer and wetting in winter, in the months preceding reactivation, moisture content increased by more than 45% throughout the slope. This is in agreement with independent data showing a significant rise in piezometric heads and shallow soil moisture contents as a result of prolonged and intense rainfall. Based on these results, remediation measures could be designed and early‐warning systems implemented. Thus, resistivity monitoring that can allow for moving electrodes provides a new means for the effective mitigation of landslide risk.

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Section: Geoelectrical Monitoring Methodologymentioning

confidence: 99%

Four‐dimensional imaging of moisture dynamics during landslide reactivation

et al. 2017

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“…discussions in Hubbard and Linde 2011). To overcome these limitations, seismic methods have been recently suggested as a complementary approach to describe the complexity of the vadose zone (e.g., Mari and Porel 2008;Grelle and Guadagno 2009;Falgàs et al 2011;Konstantaki et al 2013;Pasquet et al 2015a,b;Mari and Porel 2016;Uhlemann et al 2016;Suzaki et al 2017).…”

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confidence: 99%

Estimating picking errors in near‐surface seismic data to enable their time‐lapse interpretation of hydrosystems

Dangeard

Bodet

Pasquet

et al. 2018

Near Surface Geophysics

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Time‐lapse applications of seismic methods have been recently suggested in the near‐surface scale to track hydrological properties variations due to climate, water level changes, or permafrost thaw, for instance. But when it comes to traveltime tomography or surface‐wave dispersion inversion, a careful estimation of the data variability associated with the picking process must be considered prior to any time‐lapse interpretation. In this study, we propose to estimate picking errors that are due to the inherent subjectivity of human operators, using statistical analysis based on picking repeatability. Two seismic datasets were collected along the same profile under distinct hydrological conditions across a granite–micaschist contact at the Ploemeur hydrological observatory (France). Both datasets were recorded using identical equipment and acquisition parameters. A thorough statistical analysis is conducted to estimate picking uncertainties, at the 99% confidence level, for both P‐wave first arrival time and surface‐wave phase velocity. With the suggested workflow, we are able to identify 33% of the P‐wave traveltimes and 16% of the surface‐wave dispersion data, which can be considered significant enough for time‐lapse interpretations. In this selected portion of the data, point‐by‐point differences highlight important variations linked to different hydrogeological properties of the subsurface. These variations show strong contrasts with a non‐monotonous behaviour along the line, offering new insights to better constrain the dynamics of this hydrosystem.

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“…Renalier, Bievre, et al () demonstrate the application of these ambient noise applications using a network of seismometers to create a 3‐D image of a landslide body, but without a time series element to the data. Similarly, while some nonmonitoring studies have analyzed seismic data and inferred property distributions from active seismic measurements (Uhlemann, Hagedorn, et al, ), this has not been applied to time‐lapse seismic studies, actively or passively acquired, and represents a significant gap in the field.…”

Section: Discussionmentioning

confidence: 99%

Geophysical Monitoring of Moisture‐Induced Landslides: A Review

Whiteley

Chambers

Uhlemann

et al. 2019

Reviews of Geophysics

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204

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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. Figure 4. The geophysical methods identified in the 31 case studies, shown by mode of acquisition and method. The acronyms for each method are shown in the lower boxes.

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Landslide characterization using P- and S-wave seismic refraction tomography — The importance of elastic moduli

Cited by 78 publications

References 64 publications

Four‐dimensional imaging of moisture dynamics during landslide reactivation

Four‐dimensional imaging of moisture dynamics during landslide reactivation

Estimating picking errors in near‐surface seismic data to enable their time‐lapse interpretation of hydrosystems

Geophysical Monitoring of Moisture‐Induced Landslides: A Review

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