Rigorous Analysis of Stress-Dependent Landslide Movements with Groundwater Fluctuations Applicable to Disaster Prevention in Monsoon Asia

In this study, novel finite element approaches are proposed for numerical analysis of stress-dependent landslide movement with groundwater fluctuation by rainfall. Two new constitutive parameters that are capable of directly controlling the relationship between the apparent factor of safety and sliding velocity are incorporated into a specific material formulation used in finite element analysis for the first time. For the numerical simulation of the measured time history of the sliding displacement caused by the groundwater fluctuations, such required analytical parameters can also approximately be determined by back analysis. The proposed models are applied to a landslide field experiment on a natural slope caused by rainfall in real time in Futtsu City, Chiba Prefecture of Japan to check its applicability. The predicted and measured time histories along the horizontal direction on the upper, middle, and lower slope are compared. In addition, the deformation pattern, shear strain pattern, and possible failure mechanisms of the natural slope of such a field experiment landslide are discussed in detail based on the analysis results of the finite element method (FEM)-based numerical simulation. Moreover, the creeping landslides and possible landslide sites for further application of the proposed models are briefly discussed in the cases of Nepal and Japan as examples in Asia. It is believed that the proposed newly developed numerical models will help in understanding the secondary creep behavior of landslides triggered by extreme rainfall, and at the same time, long-term management of such landslides will be much easier in monsoon Asia. Finally, it is expected that this study will be extended for simulation of the tertiary creep behavior of landslides induced by rainfall in the near future.

The Forecasting of Groundwater Fluctuations using Time Series Analysis and Combination of Data-Driven Models

The Forecasting of Groundwater Fluctuations is a useful tool for managing groundwater resources in the mining area. Water resources management requires identifying potential periods for groundwater drainage to prevent groundwater from entering the mine pit and imposing high costs. In this research, Auto-Regressive Integrated Moving Average (ARIMA) and Holt-Winters Exponential Smoothing (HWES) data-driven models were used for short-term modeling of the groundwater fluctuations in a piezometer around the Gohar Zamin Iron Ore Mine. For this purpose, 250 non-seasonal groundwater fluctuations data in the period 22-Nov-2018 to 29-Jul-2019, 200 data for modeling, and 50 data for prediction were used. To take advantage of all the features of the two developed models, the predictions are combined with different methods and specific weights. The results show better accuracy for the ARIMA method between the two short-term forecasts, while the HWES method requires less time for modeling. Also, among all the predictions made, the highest accuracy for the combined least-squares method is for forecasting the groundwater fluctuations in the short-term. All the forecasts show a decrease in the groundwater fluctuations, indicating pumping wells around the Gohar Zamin Iron Ore Mine area.

Mapping Groundwater Fluctuations in the Coastal Los Angeles Basin by Seismic Interferometry

<p>Changes in crustal seismic velocity (d<em>v/v</em>) can be monitored continuously in time by interferometry of seismic ambient noise. This approach has been successfully employed to study temporal perturbations in stress fields, rock fractures, and fluids. Here we go one step further with this monitoring technique, by not only detecting the temporal changes but also imaging the inhomogeneous spatial distributions of d<em>v/v</em>. We implement the spatial imaging by leveraging travel-time shifts at successive lag times and solving inverse problems based on coda-wave sensitivity kernels. We then use these space-time observations of d<em>v/v </em>to investigate the groundwater fluctuations in the Coastal Los Angeles (LA) Basin during 2000-2020. Imaging of d<em>v/v</em> demonstrates the spatial patterns of groundwater variations: Seasonal changes are most pronounced within confined zones in Santa Ana Basin and LA Central Basin, whereas the long-term changes extend to a broader area including the unconfined forebay. We further compare d<em>v/v</em> observations with InSAR measurements and find strongly consistent spatial patterns. Compared with surface deformation measurements, d<em>v/v</em> additionally help to characterize the depths of several aquifers in the study area. This real-data application substantiates the validity of our d<em>v/v</em> imaging protocol, and shows the promise of using spatio-temporal d<em>v/v</em> observations to monitor surficial hydrological processes.</p>