The paper presents the results of developing a classifier for high-frequency acoustic emission signal anomalies in periods preceding and following strong earthquakes. It also provides a rationale for selecting informative signal periods relative to earthquakes onset. Generating three-dimensional displays of the dynamics of estimated amplitude distributions for this periods. A nonparametric clustering algorithm is used to structure the resulting displays. The classification algorithm is implemented using a convolutional neural network with residual connections.