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Time-lapse monitoring of climate effects on earthworks using surface waves

Bergamo, Paolo; Dashwood, Ben; Uhlemann, Sebastian; Swift, Russell; Chambers, Jonathan E.; Gunn, David A.; Donohue, Shane. 2016 Time-lapse monitoring of climate effects on earthworks using surface waves. Geophysics, 81 (2). EN1-EN15. 10.1190/geo2015-0275.1

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Abstract/Summary

The UK’s transportation network is supported by critical geotechnical assets (cuttings/embankments/dams) that require sustainable, cost-effective management, while maintaining an appropriate service level to meet social, economic, and environmental needs. Recent effects of extreme weather on these geotechnical assets have highlighted their vulnerability to climate variations. We have assessed the potential of surface wave data to portray the climate-related variations in mechanical properties of a clay-filled railway embankment. Seismic data were acquired bimonthly from July 2013 to November 2014 along the crest of a heritage railway embankment in southwest England. For each acquisition, the collected data were first processed to obtain a set of Rayleigh-wave dispersion and attenuation curves, referenced to the same spatial locations. These data were then analyzed to identify a coherent trend in their spatial and temporal variability. The relevance of the observed temporal variations was also verified with respect to the experimental data uncertainties. Finally, the surface wave dispersion data sets were inverted to reconstruct a time-lapse model of S-wave velocity for the embankment structure, using a least-squares laterally constrained inversion scheme. A key point of the inversion process was constituted by the estimation of a suitable initial model and the selection of adequate levels of spatial regularization. The initial model and the strength of spatial smoothing were then kept constant throughout the processing of all available data sets to ensure homogeneity of the procedure and comparability among the obtained V S VS sections. A continuous and coherent temporal pattern of surface wave data, and consequently of the reconstructed V S VS models, was identified. This pattern is related to the seasonal distribution of precipitation and soil water content measured on site.

Item Type: Publication - Article
Digital Object Identifier (DOI): 10.1190/geo2015-0275.1
ISSN: 0016-8033
Date made live: 09 Aug 2016 10:21 +0 (UTC)
URI: http://nora.nerc.ac.uk/id/eprint/514206

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