Microseismic Monitoring of Rockbursts with Ensemble Kalman Filter

Abstract:

Rockburst are sudden and violent releases of energy stored in rock masses, and represent one of the biggest threats in underground mining operations. These extreme events cause not only the obstruction of mining operations and damage to the infrastructure and equipment, but even worse, they threaten the life of underground workers. Assessing the risk of those hazards has become increasingly important, and microseismic monitoring is one of the main techniques to study and predict them. In this paper we introduce a new approach to forecast seismic velocity changes associated to rockburst in mines, through microseismic monitoring.

We provide a novel use of microseismic monitoring, implementing it as a tool to map the spatial distribution of wave velocities in the rock mass. We propose to use the changes in rock mass stress translated as changes in velocity of seismic waves as a complementary variable for rockburst predictions. The proposed approach begins with a multidisciplinary data integration, using Sequential Gaussian Simulation to build a 3D velocity model of the underground. Thereafter, making use of microseismic networks installed in the surroundings of a mine, traveltimes of P and S waves are assimilated on the run through an ensemble Kalman Filter, yielding almost real time updates of the initial velocity model. In addition to locating hypocenters, this allows identifying where velocity changes occur, thus highlighting zones where the rock mass is under stress and where potential hazard can be expected. The method was tested on a real 3D dataset from a deep mine in Canada, and it allowed mapping a change in velocity in the area where a rockburst occurred, four hours before it happens. This kind of result, can provide critical information to hazard risk assessment, assisting mining engineers to take safety and production decisions.