Abstract
Here we describe the development of a novel characterization and monitoring technology for unstable natural and engineered slopes. The system is based on time-lapse electrical resistivity tomography (ERT), which is a geophysical technique used to non-invasively image subsurface resistivity to depths of tens of meters. Resistivity is a useful property because it is sensitive to compositional variations, changes in moisture content, and ground movement. We have developed a low-cost system designed for remote operation, allowing resistivity images to be captured automatically and streamed via a web interface. It comprises four key elements: (1) low-power field instrumentation; (2) data telemetry and storage; (3) automated data processing; (4) and web dashboard information delivery. These elements form the basis of slope condition monitoring approach that provides near-real-time spatial information on both subsurface processes and surface responses. The use of this approach is illustrated with reference to the Ripley Landslide, a case study that demonstrates this approach as a means of spatially tracking complex subsurface moisture driven processes that would be very difficult to characterize using other approaches (e.g. surface observations or intrusive sampling). We propose that this approach could provide sub-surface information in the context of slope-scale landslide early warning systems.
| Original language | English |
|---|---|
| Title of host publication | Understanding and Reducing Landslide Disaster Risk |
| Pages | 67-74 |
| Number of pages | 8 |
| DOIs | |
| Publication status | Published - 22 Dec 2020 |
Publication series
| Name | ICL Contribution to Landslide Disaster Risk Reduction |
|---|---|
| Publisher | Springer |
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Dive into the research topics of 'Understanding and Reducing Landslide Disaster Risk'. Together they form a unique fingerprint.Student theses
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The use of near-surface geophysics for monitoring slopes that affect transport infrastructure
Holmes, J. (Author), Sivakumar, V. (Supervisor), Donohue, S. (Supervisor) & Chambers, J. (Supervisor), Dec 2021Student thesis: Doctoral Thesis › Doctor of Philosophy
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