Geophysical inversion and machine learning of dense 3D seismic and resistivity models for imaging deep landslide structures

Abstract

International audienceGeophysical imaging methods are often applied for analysing landslides; they can help detecting and localizing the landslide structures, distinguishing between lithologies, layers and slip surfaces of variable mechanical responses, and identifying the fluid storage and circulation paths.We present a joined analysis of two 3D geophysical models acquired from a dense electrical resistivity tomography (Fullwaver nodes) and a dense seismic travel-time tomography (DENSAR nodes) acquired at the Viella landslide (Hautes-Pyrenees, France). Fusion of the two geophysical models and of ancillary geotechnical and hydrogeological information from boreholes is carried out using machine learning, in order to predict the location of the different slope structures