1887

Abstract

Summary

Combining geophysical methods allows for the characterisation and monitoring of subsurface processes in landslide systems at unprecedented spatiotemporal resolutions. For high spatial resolution characterisation and monitoring of the subsurface, methods that provide maps, cross-sections and three-dimensional volumes of data are preferred. An overview of the results of various long-term monitoring campaigns using such geophysical methods at the Hollin Hill Landslide Observatory in the UK are presented. These methods include electrical resistivity and seismic tomography, self-potential mapping and cross-sections of horizontal-to-vertical ratio measurements of ambient seismic noise. Repeating these surveys over time results in the production of time-lapse data, making these approaches effective monitoring tools. Variations in these measurements show relationships to changes in environmental conditions, for example, decreases in seismic velocity and resistivity values associated with decreases in soil moisture content. Critically, the use of geotechnical-geophysical relationships can provide information between, and beyond the depth of, shallow geotechnical and surface environmental sensors. Using such time-series of high resolution spatial data can help achieve a better understanding of the moisture and kinematic dynamics of unstable slopes, and provides subsurface information for incorporation in to local landslide early warning systems.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201902450
2019-09-08
2020-09-21
Loading full text...

Full text loading...

References

  1. Bièvre, G., Jongmans, D., Winiarski, T. & Zumbo, V.
    2012. Application of geophysical measurements for assessing the role of fissures in water infiltration within a clay landslide (Trièves area, French Alps). Hydrological Processes, 26, 2128–2142.
    [Google Scholar]
  2. Castellaro, S., Mulargia, F. & Bianconi, L.
    2005. Passive seismic stratigraphy: A new efficient, fast and economic technique. J. Geotech. Environ. Geol., 3, 51–77.
    [Google Scholar]
  3. Colangelo, G., Lapenna, V., Perrone, A., Piscitelli, S. & Telesca, L.
    2006. 2D Self-Potential tomographies for studying groundwater flows in the Varco d’Izzo landslide (Basilicata, southern Italy). Engineering Geology, 88, 274–286.
    [Google Scholar]
  4. Grandjean, G., Hibert, C., Mathieu, F., Garel, E. & Malet, J.-P.
    2009. Monitoring water flow in a clay-shale hillslope from geophysical data fusion based on a fuzzy logic approach. Comptes Rendus Geoscience, 341, 937–948.
    [Google Scholar]
  5. Imposa, S., Grassi, S., Fazio, F., Rannisi, G. & Cino, P.
    2017. Geophysical surveys to study a landslide body (north-eastern Sicily). Natural Hazards, 86, 327–343.
    [Google Scholar]
  6. Jongmans, D. & Garambois, S.
    2007. Geophysical investigation of landslides : a review. Bulletin De La Societe Geologique De France, 178, 101–112.
    [Google Scholar]
  7. Mccann, D. M. & Forster, A.
    1990. Reconnaissance geophysical methods in landslide investigations. Engineering Geology, 29, 59–78.
    [Google Scholar]
  8. Pecoraro, G., Calvello, M. & Piciullo, L.
    2019. Monitoring strategies for local landslide early warning systems. Landslides, 16, 213–231.
    [Google Scholar]
  9. Uhlemann, S., Chambers, J., Wilkinson, P., Maurer, H., Merritt, A., Meldrum, P., Kuras, O., Gunn, D., Smith, A. & Dijkstra, T.
    2017. Four-dimensional imaging of moisture dynamics during landslide reactivation. Journal of Geophysical Research: Earth Surface, 122, 398–418.
    [Google Scholar]
  10. Uhlemann, S., Hagedorn, S., Dashwood, B., Maurer, H., Gunn, D., Dijkstra, T. & Chambers, J.
    2016. Landslide characterization using P- and S-wave seismic refraction tomography — The importance of elastic moduli. Journal of Applied Geophysics, 134, 64–76.
    [Google Scholar]
  11. Whiteley, J. S., Chambers, J. E., Uhlemann, S., Wilkinson, P. B. & Kendall, J. M.
    2019. Geophysical Monitoring of Moisture-Induced Landslides: A Review. Reviews of Geophysics, 57, 106–145.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201902450
Loading
/content/papers/10.3997/2214-4609.201902450
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error