Ionospheric Alfvén Resonances (IARs) are weak discrete non-stationary Alfvén waves along magnetic field lines, at periods of í0.5–20 Hz, that occur during local night-time, particularly during low geomagnetic activity. They are detectable through time-frequency analysis (spectrograms) of measurements made by sensitive search coil magnetometers. The IARs are generated by the interaction of electromagnetic energy partially trapped in the Earth-ionosphere cavity with the main geomagnetic field and their behavior provides proxy information about atmospheric ion density between 100–1000 km altitude. Limited methods exist to automatically detect and analyse their properties and behavior as they are difficult to extract using standard image and signal processing techniques. We present a new method for the detection of IARs based on the fully convolutional neural network U-net. U-net was chosen as it is able to perform accurate image segmentation and it can be trained in a supervised fashion on a relatively small labeled dataset utilizing data augmentation. We show that the resulting predictive model generated by training the U-net is able to detect IAR signals while mislabelling considerably less noise than other data analysis methods. We achieved our best results by using a training set of 178 hand-digitized examples from high-quality spectrograms measured at the Eskdalemuir Geophysical Observatory (UK). We find that the network converges in ten iterations with a final intersection over union (IoU) metric of 0.9 and a training loss of below 0.2. We use the trained network to extract IARs from over 2300 images, covering six years of search coil magnetometer data measured at the Eskdalemuir Observatory. U-net can also automatically handle missing data or days without IARs, giving a null result as expected. This constitutes the first use of a neural network for pattern recognition of unstructured image data such as spectrograms containing IAR signals, though the method is applicable to other types of resonances or geophysical features in the time-frequency domain.
- Data processing
- Image analysis
- Parallel and high-performance computing