Method for detecting anomalies in geomagnetic field variations based on artificial neural network

Abstract

The paper proposes a method for anomaly detection in geomagnetic data based on the classical autoencoder architecture. The training data consisted of daily variations in the geomagnetic field on quiet days for 2020, 2021, and 2022, collected from the Ak-Suu base station of the geomagnetic monitoring network of the Research Station of the Russian Academy of Sciences in Bishkek. The neural network has five hidden layers with a total of ~3.5‧106 trainable parameters. The trained model accurately reproduces typical features of normal data, whereas in the presence of anomalies it shows a decline in reconstruction quality. This property of the autoencoder was used to classify the data into two categories: normal and anomalous. The reconstruction error, measured as the Mean Absolute Error (MAE), was used as the anomaly metric. In particular, the MAE value of 0.109 was used as the threshold for class separation. Testing the model on the data from the Ak-Suu station for 2017, 2018, and 2019 demonstrated good results. Binary classification metrics such as recall and F1-score were notably high: 0.965 and 0.918 for the 2017 data, 0.982 and 0.933 for the 2018 data, and 0.970 and 0.935 for the 2019 data, respectively.

Keywords

• anomaly
• geomagnetic field
• variational series
• neural network
• autoencoder
• confusion matrix