Abstract
In the paper, a new approach to identifying zones with rare anomalous manifestations of geological processes is proposed. The approach is based on two one-class classification methods of machine learning: the method of minimum area of alarm and the method of preference. The algorithm of minimum area of alarm is nonparametric. It is trained on a sample of anomalous events and computes the field of anomalous zones. The knowledge obtained by this method is non-verbalized. The method of preference allows approximating the obtained solution by a rather simple logical rule that defines the anomalous region in terms of analyzed properties of the geological environment. Examples of this approach to finding areas of possible foci of strong earthquakes and to make a regional forecast of deposits are considered.
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