Geomagnetic data prediction with deep learning under the constraint of reference channel data

Abstract

Geomagnetic data are crucial for pre‐earthquake electromagnetic anomaly analysis and deep earth exploration, yet data acquisition is highly susceptible to cultural noise, instrument failures or transmission anomalies, leading to data loss or saturation. For decades, time‐series editing and reference station methods have evolved independently. Time‐series denoising without reference constraints risks over‐ or under‐processing, whereas reference station methods demand strictly high‐quality reference data, lacking flexibility. Integrating their strengths, this paper proposes a reference‐channel‐constrained deep learning method for geomagnetic noise suppression. First, a novel network named GATCN—combining a gated recurrent unit, attention mechanism and temporal convolutional network (TCN)—is proposed for the rapid, high‐precision identification of high‐quality or anomalous signal segments. Second, under the constraint of synchronous reference data, multivariate variational mode decomposition (MVMD) decomposes the identified high‐quality signals into distinct frequency components. Third, a convolutional neural network (CNN)–long short‐term memory network (LSTM) network predicts high‐quality signals for noisy or anomalous segments based on the decomposed components, utilizing the same synchronous reference constraints. Finally, the identified and predicted high‐quality signals are chronologically concatenated to reconstruct the complete data. Validation using data from the Western Pacific seafloor and the Chinese mainland demonstrates that GATCN achieves 99.25% recognition accuracy with robust feature extraction and stable gradient propagation, outperforming state‐of‐the‐art models like ResNet, TCN, transformer and IncepTCN. Moreover, the MVMD and CNN–LSTM‐based prediction approach surpasses advanced networks, such as transformer, CNN–transformer, LSTM and bi‐directional LSTM (BiLSTM). Ultimately, compared to existing time‐series methods, our approach substantially enhances reliability and robustness against varying noise intensities and types; compared to the magnetotelluric remote reference method, it drastically lowers reference data requirements, yielding significantly greater flexibility.