Ambient noise shear-wave tomography for shallow landslide structural models retrieval from dense 3D seismological arrays.

The versatility, cost-efficiency and easy deployment of seismic sensor nodes facilitate geophysical monitoring in environments that were previously inaccessible for instrumentation, and among them landslides and unstable slopes, most of the time located in remote mountains. Using nodes allows for the setup of dense arrays with sensor inter-trace distances that become compatible with the geometries and dimensions of the geological structures to image. This becomes particularly true for landslides which have complex 3D architectures (hummocky bedrocks, layering, multi-dimensional fractures, diverse geotechnical material, deep and perched aquifers and water circulations) and are shallow processes with respect to the classical investigation depths and sensitivity of most geophysical survey techniques.

Here we develop a specific processing workflow to allow the computation of 3D shear-velocity models with Ambient-Noise-based tomography applied to dense arrays of seismic stations. The workflow is applied to a dataset acquired at the Viella shallow landslide (France) developed in altered schists and moraine deposits. We deployed 70 IGU-16HR-3C-5Hz SmartSolo sensors (EOST/PISE service) with inter station distances of 70 m for a period of 25 days.

The processing consists in several steps, all of them being tuned to the specific case of shallow depths of investigation. In areas where only few strong (M_L>4) earthquakes are triggered, with a low azimuthal distribution, surface-waves velocity fields are complex to estimate with earthquakes. Ambient noise cross-correlation tomography has the advantage of using the ambient noise to model the surface waves velocities by retrieving the interstation Green’s functions. The main hypothesis for retrieving the Green’s functions is a homogeneous noise-source distribution, which is never achieved in a natural environment. Therefore, data filtering and daily stacking are crucial to reduce the effect of non-uniform noise distributions and lead to consistent velocity models. Due to the noisy environment of Viella (torrential flows, farming activity, anthropogenic noise), several procedures were implemented to optimize the processing (reduction of the coherent noises in the processed data, use of a pseudo-topography to estimate as accurately as possible the inter-station distances and travel times). We then computed the dispersion curve diagrams for the surface waves on which we applied a strict selection to only keep the consistent part of the surface waves dispersion curves. The selection parameters were optimized for the Rayleigh and Love waves. Then, we inverted the inter-station travel times to compute group velocities maps at several frequencies. Finally, we proceed to a Markov-Chain-Monte-Carlo inversion of each of the dispersion curves extracted from the group velocity maps. We finally obtained a 3D shear velocity model, which is further combined with geological and borehole information in order to document the 3D structure.

The objectives are to present the processing workflow developed specifically for shallow imaging and the retrieval of 3D heterogeneities; effects of the processing parameters will be discussed on the Viella dataset. The approach developed for Viella is generic and has been further applied to other geological processes (permafrost at the Chauvet rock glacier, Marie-sur-Tinée mudslide), and the models will be discussed.