Aurélien Vallet

Prediction of displacement rates at an active landslide using joint inversion of multiple time series

This work focuses on the development of FLAME (Forecasting Landslides induced by Acceleration Meteorological Events) that analyze of the relationship between displacements and precipitations using a statistical approach in order to predict the surface displacement at active landslide. FLAME is an Impulse Response model (IR) that simulates the changes in landslide velocity by computing a transfer function between the input signal (e.g. rainfall or recharge) and the output signal (e.g. displacement). This model has been applied to forecast the displacement rates at Sechilienne (French Alps). The FLAME model is enhanced by achieving the calibration using joint inversion of multiple time series data. We consider that the displacements at two different sensors are explained by the same long-term response of the system to ground water level variations. The parameters describing the long-term response of the system are therefore identical for all sensors. The joint inversion process allows decreasing the ratio between the number of parameters to be inverted and the volume of data and is thus more statically steady. The results indicate that the models are able to reproduce the displacement pattern in general to moderate kinetic regime but not extreme kinetic regime. Our results do not give clear evidence of an improvement of the models performance with joint inversion of multiple time series of data. The reasons which could explain these inconclusive results are discussed in the paper.

A multi-dimensional statistical rainfall threshold for deep landslides based on groundwater recharge and support vector machines

The rainfall threshold determination is widely used for estimating the minimum critical rainfall amount which may trigger slope failure. The aim of this study was to develop an objective approach for the determination of a statistical rainfall threshold of a deep-seated landslide. The determination is based on recharge estimation and a multi-dimensional rainfall threshold. This new method is compared with precipitation and with a conventional ‘two-dimensional’ rainfall threshold. The method is designed to be semiautomatic, enabling an eventual integration into a landslide warning system. The method consists in two independent parts: (i) unstable event identification based on displacement time series and (ii) multi-dimensional rainfall threshold determination based on support vector machines. The method produces very good results and constitutes an appropriate tool to define an objective and optimal rainfall threshold. In addition to shortened computation times, the non-necessity of pre-requisite hypotheses and a fully automatic implementation, the newly introduced multi-dimensional approach shows performances similar to the classical two-dimensional approach. This shows its relevance and its suitability to define a rainfall threshold. Lastly, this study shows that the recharge is a relevant parameter to be taken into account for deep-seated rainfall-induced landslides. Using the recharge rather than the precipitation significantly improves the delineation of a rainfall threshold separating stable and unstable events. The performance and accuracy of the multi-dimensional rainfall threshold developed for the Séchilienne landslide make it an appropriate method for integration into the present-day landslide warning system.

Hydrogeological Threshold Using Support Vector Machines and Effective Rainfall Applied to a Deep Seated Unstable Slope (Séchilienne, French Alps)

Rainfall threshold is a widely used method for estimating minimum critical rainfall amount which can yield a slope failure. Literature reviews show that most of the threshold studies are subjective and not optimal. For this study, effective rainfall was considered for threshold definition. Support vector machines (SVM) and automatic event identification were used in order to establish an optimal and objective threshold for the Sechilienne landslide. Effective rainfall does significantly improve threshold performance (misclassification rate of 7.08 % instead of 13.27 % for gross rainfall) and is a relevant parameter for threshold definition in deep-seated landslide studies. In addition, the accuracy of the Sechilienne SVM threshold makes it appropriate to be integrated into a landslide warning system. Finally, the ability to make predictions at a daily time step opens up an opportunity for destabilisation stage predictions, through the use of weather forecasting.

Hydrochemical Approach of Mechanical Degradation of the Séchilienne Unstable Slope

Water chemistry is a very fine signal which allows fine location in time and space of the arrival of infiltration water inducing mechanical instability pulses of the landslide. This tool is designed to understand the complex relationship between chemical weathering, hydromechanical changes and weakening/motion of the unstable rock slope. For this purpose, a hydrogeochemical groundwater monitoring has been established since 2010 on the site of Sechilienne (France). Electrical conductivity is representative of the chemical signal generated by the degradation of the massif. The continuous measurement of this parameter is relevant to the site of Sechilienne and can replace chemical monitoring. The benefit of acquiring this data is threefold: real-time measurements, with a short time step, and inexpensive implementation work, enabling to use it as a tool for risk management.