Since earthquakes are a global-scale problem, humanity has been attempting to predict them for a long time. Earlier [1], it was shown that machine learning can be used to predict earthquakes. Nevertheless, a sufficiently accurate and complete predictive model could not be obtained, which may be due to an insufficient number of features. In this paper, anomalies in the ionosphere preceding seismic activity are considered as earthquake precursors. Two existing approaches to detecting ionosphere anomalies were considered; a third one was proposed, using readings of several ionosondes located in the neighborhood of the earthquake area or in a ring around such neighborhood. To test these approaches, a collection of ionosphere characteristics data, obtained from ground ionosondes, was gathered and processed. In the future, discovered anomalies are planned to be used as features for machine learning models.