The hazard mitigation of rockslides and the understanding of their time evolution are a main task particularly when a pre-existing fracture system affects the rock mass. Instability can rapidly accelerate to failure without clear precursory warnings. Traditional monitoring techniques (inclinometers, tiltmeters, extensometers, ...) may be inadequate to forecast the dynamics of evolution and provide quantitative elements for the development of an early warning system. Since a persistent fracture system originates from the genesis, growth and progressive coalescence of microcracks, it is key to constrain the initial stages of rupture. The energy release, in the form of elastic waves, associated with the formation of microcracks can be detected by a microseismic monitoring network. The location of the microseismic events and the monitoring of their time and spatial evolution with particular reference to potential destabilizing factors, such as rainfalls and temperature changes, can provide information with respect to the understanding of the unstable sectors within the rock mass and the prediction of the possible acceleration to the failure. In this paper we present the first results from a microseismic monitoring network installed at Madonna del Sasso (NW Italy) to monitor rock instability phenomenon highlighted by neat and long lasting episodes of slow deformation recorded by standard monitoring devices (topographic measurements, borehole inclinometers, crackmeters and extensometers). A detailed geophysical and geomechanical characterization of the site has been preliminarily undertaken in order to define the overall geometry and the fracturing state of the instable rock mass and to establish the best position of the nodes for the monitoring network. Furthermore the definition of the seismic velocity field inside the rock mass is a fundamental parameter for the processing of the recorded microseismic signals, in order to localize the microseismic events. We also analyzed the signals acquired during the first six months of monitoring. We present the first results, and we aim to develop objective and automatic procedures for signal classification and event localization.
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