基于地空频谱在线学习的地震前电磁异常检测

提出了一种应用于噪声环境下的多尺度卷积神经网络(CNN)在线地震前电磁异常检测模型。该模型在CNN强大特征提取能力的基础上，通过多尺度机制协同长短期地空电磁频谱特征，多维度、多视角地开展对地震前电磁的异常检测。同时引入自适应变分模态分解(VMD)降噪方法提取观测信号中的有效信息，最后配合在线学习策略，实现对地震前电磁异常模式可能变化的持续学习。仿真结果表明，多尺度模型在低信噪比下能够保持较高的准确率，在线学习策略能够有效缩短模型更新时间，由此证明了模型的有效性。

Pre-earthquake electromagnetic anomaly detection based on online learning of ground space spectrum in multi-scale CNN

This paper proposes a multi-scale Convolutional Neural Network(CNN) online pre- earthquake electromagnetic anomaly detection model which is applied in noisy environment. Based on the powerful feature extraction ability of CNN, cooperating with the characteristics of long-term and short-term ground-space electromagnetic spectrum, the pre-earthquake electromagnetic anomaly detection is performed in multi-dimensional and multi-perspective. At the same time, the adaptive Variational Mode Decomposition(VMD) noise reduction method is introduced to extract the effective information in the observation signal. Combined with online learning strategy, the continuous learning of possible changes of pre-earthquake electromagnetic anomaly mode is realized. The simulation results show that the multi-scale model can maintain high accuracy under low Signal-to-Noise Ratio(SNR), and the online learning strategy can effectively reduce the model update time, which proves the effectiveness of the model.