Abstract
Quick clay is a known hazard in formerly-glaciated coastal areas in e.g., Scandinavia and Canada, and hence significant efforts are being taken to map their occurrence and extent. Quick-clay landslide prone areas are usually investigated only by geotechnical means, but recently, considerable efforts by a number of researchers have been made to investigate areas of sensitive clay using a range of geophysical techniques. Although the majority of this work has focused on measurements of electrical resistivity, other geophysical techniques (electromagnetic and seismic) have also received attention in the literature. Although it was recognized that some intrusive geotechnical investigations will always be necessary, the objective of these studies was to develop techniques to maximize the use of non-intrusive geophysical surveys.
As a result of intensive research in the past thirty years, particularly in Norway, Sweden and eastern Canada, the effects of post-depositional physical and chemical processes on the engineering properties of soft clays are now fairly well understood. The importance of geological and physico-chemical factors in the interpretation and analysis of such geotechnical problems as landslides and the settlement of structures has been clearly recognized. Therefore, following a thorough review of the physical properties of quick clays, we evaluated the potential of geophysics for quick-clay investigation in order to find a suitable, integrated and multi-disciplinary approach to improve our possibilities to accurately identify its occurrence and map its extent both vertically and laterally.
Using a number of case study, we demonstrate how geophysics can contribute to better investigate sites prone to quick-clay landsliding and advantageously complement geotechnical localized 1D soundings by providing detailed stratigraphic and quantitative information in 2D and 3D. Since geophysics does not directly provide the necessary parameters for quick-clay characterization, one as to link geophysical parameters to geotechnical ones through, e.g., empirical correlations. We therefore also explored potential correlation between geotechnical and geophysical parameters.
Having different dataset to interpret, we perform data integration using data fusion by fuzzy logic or cluster analysis. Another alternative is to directly invert all of the available experimental data using a joint inversion algorithm. The resulting model can then be interpreted more easily and with more confidence since joint-inversion reduces the inversion uncertainty of each separate methods. The joint-inversion algorithm was developed in collaboration with Flora Garofalo, Ph.D. research fellow at Politecnico di Torino, Italy.
Finally, we show how the geological model resulting from geotechnical and geophysical data integration can be used for landslide site characterization and stability assessment.