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.

在本文中，提出了一种新的方法来识别具有罕见异常地质过程的区域。该方法基于机器学习的两种一类分类方法：最小警报面积方法和偏好方法。最小警报面积算法是非参数的。对异常事件的样本进行训练，并计算异常区域的范围。通过这种方法获得的知识是非语言化的。优选方法允许通过相当简单的逻辑规则来近似所获得的解，该逻辑规则根据所分析的地质环境的性质来定义异常区域。考虑了这种方法的示例，该方法用于查找强震可能的震源区域并做出区域性的沉积物预测。