The Schumann resonance (SR) signal has attracted much attention as a potential earthquake precursor indicator. To enable rapid identification of these signals from massive volumes of China Seismo-Electromagnetic Satellite (CSES) data, this paper presents a machine learning-based image recognition algorithm. Firstly, the Ultra-Low Frequency (ULF) band power spectrum data of the ionospheric electric field was standardized to enhance the visual contrast of the signal and generate a spectrogram. A small-image dataset with standardized image size and labeled positive and negative samples was constructed by cropping the original images. High-dimensional features of the image were extracted using the deep convolutional neural network VGG16 algorithm, combined with the support vector machine (SVM) algorithm to classify whether the high-dimensional data contains SR signals. The sliding window recognition algorithm is designed to process large-format power spectrum images. The results showed that this VGG16-SVM hybrid model achieved an accuracy of 95.00% on the independent small-image test set, which was superior to both pure SVM and pure VGG16 models. On the large-format image prediction set, the overall accuracy of the model is 81.48%, and the SR physical properties of the recognition signal are verified through frequency statistics. The hybrid model was applied to the SR detection and recognition of the Yangbi earthquake in Yunnan, China, and achieved ideal results. This indicates that the proposed VGG16-SVM hybrid model can quickly and effectively identify SR signals in CSES data, which has important practical value for automated electromagnetic signal analysis in seismic research.
Zhu et al. (Thu,) studied this question.