Integrated time-lapse geophysical imaging and remote-sensing study of the antropoghenic triggering of the landslides

In recent years, rapid climatic changes and their impact is widely visible and recognizable around the world. One of the effects of global warming is reduced snow cover in high-mountain areas. Such a situation leads to the case, where retaining snow cover suitable for the skiing activities is crucial. As a solution, heavy artificial snow with high water content is used. To prolongate the skiing season additional snow is produced and stored as a thick cover on the hillsides.  Such a heavy load leads to a situation, where slow-developing landslides with a tendency for rapid movements can occur. Such a situation can be potentially dangerous not only for the infrastructure but also for the humans themselves. Such a situation was observed in a study site in Cisiec (Silesian Voivodeship, Southern Poland), we're slowly developing landslide strongly affected the infrastructure on a small skiing resort. For a fuller understanding of the problem, precise geophysical imaging is required to distinguish of main triggering factors, as well as the anthropogenic impact on the landslide itself. In the presented study, the authors propose an integrated geophysical approach utilizing imaging techniques such as seismic reflection imaging and tomography, seismological monitoring, Electrical resistivity tomography (ERT), Audio-Magnetotellurics (AMT), laser scanning and photogrammetry for the monitoring time evolution of anthropogenically developed landslide. The integration of the results allows for obtaining a more certain image of the subsurface and its time evolution necessary for the studied problem. By using the uncertainty driven approach, where data is correlated with preserved information about its uncertainty, multiple interpretation mistakes can be solved. As a result, the authors were able to estimate the seasonal evolution of the landslide in relationship to the anthropogenic load on the hillside.

This research was funded by the National Science Centre, Poland (NCN) Grant 2020/37/N/ST10/01486.