Sensor Fusion for Monitoring Unstable Rock Slopes - A Case Study from the Stampa Instability, Norway

The unstable rock slope Stampa is located north-east of the touristic town of Flåm, Norway along the Aurlandfjord and displays signs of post-glacial deformation over a large area and a volume of several million m³. Directly below the rock slope lies the European Road E16, a highly frequented connection between Bergen and Oslo. Two high-risk objects have been identified on the instability, which are currently being monitored continuously by the Norwegian Energy and Water Directorate. The Landslide Research Group at Western Norway University of Applied Sciences uses an object on the unstable rock slope, Block 4a, as a field laboratory for sensor networks. The approximately 5,000 m³ Block sits on a highly fractured base of approximately 40,000 m³ and has recently been moving at speeds in excess of 1 cm per day. Different failure scenarios threaten the European Road under the object and potentially the town of Flåm. Data from an on-site sensor network with a range of instruments such as wire-extensometer, inclinometer, temperature loggers and geophones has been collected over a period of three years and combined with remote sensing data from a robotic total station, ground-based InSAR and satellite-based InSAR with the use of a corner reflector as persistent scatterer as well as weather station data from Stampa. Sensor Fusion has been used to merge the data of the different sensors and exploit the different resolutions of the respective sensors. This led to the development of a data set with high spatiotemporal resolution capturing the physical properties of Block 4a, such as displacement direction and velocity. This approach makes use of complementary sensor data to fill gaps in time series of other sensors, which can be caused by sensor faults or are due to sensor down-times during maintenance. Both the sensor fusion approach as well as filtering of outliers requires expert knowledge about the system in question, which sensor fusion research groups often do not integrate into their analysis. We propose thus a holistic analysis approach at the intersection between data science and geology. Preliminary analyses of the augmented data for Block 4a confirm high displacement rates at the end of 2022. This follows a general trend of acceleration that has been observed over the last three years. Furthermore, the displacement accelerations seem to follow a seasonality, with acceleration phases in spring and autumn, while summer and winter coincide with less movement. Based on the sensor fusion analysis we can identify that rain fall periods in autumn as well as snowmelt in spring have an impact on the block displacement. However, we conclude that precipitation alone cannot explain acceleration phases. Instead, we propose a model based on the combined influence of rain and snowmelt paired with air and rock surface temperature on the slope movement. In combination with a refined sensor fusion process, we expect our work to be transferable and relevant for the monitoring of other unstable rock slopes.