Abstract <p>The proposed approach is based on the assumption, known from laboratory experiments, that the current frictional properties of the slip surface are reflected both in the source parameters of individual induced microseismic events and in the characteristics of seismoacoustic noise, the sources of which are localized in the fault zone. The technique is estimates the scaled seismic energy, which makes it possible to judge the probability of the realization of the elastic energy accumulated in a massif in the form of dynamic events. On the example of data recorded at the Korobkovskoe iron ore deposit of the Kursk Magnetic Anomaly, the source parameters of seismic events induced by explosions were estimated. On the basis of the results, it is shown that a swarm of induced microearthquakes with a low rupture propagation velocity was detected at the KMA-ruda mine. The prospects of using machine learning methods to determine the time and magnitude of an impending dynamic event in real time based on the data of laboratory experiments with AE are shown. The results can be used for short-term forecasting of large dynamic events under the conditions of an operating mine. The analysis showed the promise of creating new methods for monitoring stressed massifs during mining operations in order to prevent the initiation of large earthquakes associated with dynamic displacement along tectonic faults.</p>