M. Rossi | USGS Science

USGS2017

Inflation leading to a Slow Slip Event and volcanic unrest at Mt. Etna in 2016: Insights from CGPS data

Global Positioning System (CGPS) data from Mount Etna between May 2015 and September 2016 show intense inflation and a concurrent Slow Slip Event (SSE) from 11 December 2015 to 17 May 2016. In May 2016, an eruptive phase started from the summit craters, temporarily stopping the ongoing inflation. The CGPS data presented here give us the opportunity to determine (1) the source of the inflating body, (2) the strain rate parameters highlighting shear strain rate accumulating along NE Rift and S Rift, (3) the magnitude of the SSE, and (4) possible interaction between modeled sources and other flank structures through stress calculations. By analytical inversion, we find an inflating source 5.5 km under the summit (4.4 km below sea level) and flank slip in a fragmented shallow structure accommodating displacements equivalent to a magnitude M w 6.1 earthquake. These large displacements reflect a complex mechanism of rotations indicated by the inversion of CGPS data for strain rate parameters. At the scale of the volcano, these processes can be considered precursors of seismic activity in the eastern flank of the volcano but concentrated mainly on the northern boundary of the mobile eastern flank along the Pernicana Fault and in the area of the Timpe Fault System.

Mt. Etna↗

USGS2013

Improving predictive power of physically based rainfall-induced shallow landslide models: a probablistic approach

Distributed models to forecast the spatial and temporal occurrence of rainfall-induced shallow landslides are deterministic. These models extend spatially the static stability models adopted in geotechnical engineering and adopt an infinite-slope geometry to balance the resisting and the driving forces acting on the sliding mass. An infiltration model is used to determine how rainfall changes pore-water conditions, modulating the local stability/instability conditions. A problem with the existing models is the difficulty in obtaining accurate values for the several variables that describe the material properties of the slopes. The problem is particularly severe when the models are applied over large areas, for which sufficient information on the geotechnical and hydrological conditions of the slopes is not generally available. To help solve the problem, we propose a probabilistic Monte Carlo approach to the distributed modeling of shallow rainfall-induced landslides. For the purpose, we have modified the Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability Analysis (TRIGRS) code. The new code (TRIGRS-P) adopts a stochastic approach to compute, on a cell-by-cell basis, transient pore-pressure changes and related changes in the factor of safety due to rainfall infiltration. Infiltration is modeled using analytical solutions of partial differential equations describing one-dimensional vertical flow in isotropic, homogeneous materials. Both saturated and unsaturated soil conditions can be considered. TRIGRS-P copes with the natural variability inherent to the mechanical and hydrological properties of the slope materials by allowing values of the TRIGRS model input parameters to be sampled randomly from a given probability distribution. The range of variation and the mean value of the parameters can be determined by the usual methods used for preparing the TRIGRS input parameters. The outputs of several model runs obtained varying the input parameters are analyzed statistically, and compared to the original (deterministic) model output. The comparison suggests an improvement of the predictive power of the model of about 10% and 16% in two small test areas, i.e. the Frontignano (Italy) and the Mukilteo (USA) areas, respectively. We discuss the computational requirements of TRIGRS-P to determine the potential use of the numerical model to forecast the spatial and temporal occurrence of rainfall-induced shallow landslides in very large areas, extending for several hundreds or thousands of square kilometers. Parallel execution of the code using a simple process distribution and the Message Passing Interface (MPI) on multi-processor machines was successful, opening the possibly of testing the use of TRIGRS-P for the operational forecasting of rainfall-induced shallow landslides over large regions.

Geoscientific Model Development and Discussions↗