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Using marine resistivity to map geotechnical properties: a case study in support of dredging the Panama CanalNormal access

Authors: D.F. Rucker and G.E. Noonan
Issue: Vol 11, No 6, December 2013 pp. 625 - 637
DOI: 10.3997/1873-0604.2012017
Special Topic: Geotechnical Assessment and Geoenvironmental Engineering
Language: English
Info: Article, PDF ( 5.13Mb )

The distribution of subbottom geotechnical strength properties within the Panama Canal are needed to help with the Canal’s expansion. Core data already exist in the Canal, including lithological/stratigraphical descriptions and qualitative measurements of rock hardness. These data have been acquired within the Canal during previous expansion activities conducted over the past 60 years. Alone, the core data can be used to estimate rock hardness at unsampled locations using geostatistical methods. However, to help reduce uncertainty in the interpolation of rock hardness, a spatially continuous electrical resistivity survey was conducted to provide a better means of bridging information between cores. Although no direct causative link between rock hardness and resistivity exists, it was thought that the resistivity would be dependent upon jointly influencing parameters that comprise the geomechanical attributes of the rock, in this case porosity. For example, tuff generally had lower hardness and lower resistivity values compared to andesite and differences in porosity of these rock types would help explain the trend. When considering the resistivity in this geologic context, the spatial interpolation of rock hardness showed better agreement with measured data at sampled locations compared to methods that did not consider any geological context (including kriging of core data or a polynomial regression model between resistivity and rock hardness). Additionally, it is believed that full three-dimensional inverse modelling of the resistivity data helped to correctly resolve the location of low-resistivity features that could have been detected as off-line effects in two-dimensional processing algorithms. With these results, it is anticipated that the costs of dredging could be reduced by the simple fact that necessary resources can be anticipated for some of the harder rock types.

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