4D electrical resistivity to monitor unstable slopes in mountainous tropical regions: an example from Munnar, India

Abstract

The number of large landslides in India has risen in the recent years, due to an increased occurrence of extreme monsoon rainfall events. There is an urgent need to improve our understanding of moisture-induced landslide dynamics, which vary both spatially and temporally. Geophysical methods provide integrated tools to monitor subsurface hydrological processes in unstable slopes at high spatial resolution. They are complementary to more conventional approaches using networks of point sensors, which can provide high temporal resolution information but are severely limited in terms of spatial resolution. Here, we present and discuss data from an electrical resistivity tomography monitoring system—called PRIME—deployed at the Amrita Landslide Early Warning System (Amrita-LEWS) site located in Munnar in the Western Ghats (Kerala, India). The system monitors changes in electrical resistivity in the subsurface of a landslide-prone slope that directly threatens a local community. The monitoring system provides a 4D resistivity model informing on the moisture dynamics in the subsurface of the slope. Results from a 10-month period spanning from pre-monsoon to the end of the monsoon season 2019 are presented and discussed with regard to the spatial variation of soil moisture. The temporal changes in resistivity within the slope are further investigated through the use of time-series clustering and compared to weather and subsurface pore water pressure data. This study sheds new light on the hydrological processes occurring in the shallow subsurface during the monsoon and potentially leading to slope failure. This geophysical approach aims at better understanding and forecasting slope failure to reduce the risk for the local community, thereby providing a powerful tool to be included in local landslide early warning systems.

Electrical Resistivity Changes During Heating Experiments Unravel Heterogeneous Thermal‐Hydrological‐Mechanical Processes in Salt Formations

“…Applying simple machine learning algorithms to temporal data sets can facilitate the classification of resistivity models into zones that exhibit relatively consistent dynamics over time (Delforge et al., 2021; Watle et al., 2023). The primary objective of clustering is to identify areas potentially affected by rock salt damage or other processes of interest.…”

An Iterative 3D Correction plus 2D Inversion Procedure to Remove 3D Effects from 2D ERT Data along Embankments

“…The most common applications include detection of different types of subsurface voids and cavities [ 17 , 18 , 19 , 20 ], mineral exploration [ 21 , 22 ], characterization of landfills, mining dumps and heap leaching facilities [ 23 , 24 , 25 , 26 , 27 ], and groundwater studies [ 28 , 29 ]. In recent years, the ERT method has continuously proven itself as an efficient element to be integrated in hydrogeological risk mitigation strategies, thanks to its potential to monitor changes in water saturation of the subsurface material and to detect seepage zones [ 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 ].…”

“…Sensors 2024, 24, x FOR PEER REVIEW 2 of 20 risk mitigation strategies, thanks to its potential to monitor changes in water saturation of the subsurface material and to detect seepage zones [30][31][32][33][34][35][36][37][38][39][40][41]. This paper specifically addresses ERT monitoring of earthen embankments like river levees, earthen dams, tailings dams and transportation embankments [1,[11][12][13]32,39].…”