Research on magnetic anomaly signal detection based on CNN-LSTM

Abstract Due to the stable propagation of magnetic anomaly signal in ocean and air, magnetic anomaly detection technology has become an effective means for non-acoustic detection. Deep learning, particularly convolutional neural networks (CNN), has made significant progress in underwater signal detection. CNN has become an important tool in this field due to its advantages in handling large-scale data, automatic feature extraction, and pattern recognition. However, magnetic anomaly signal is often accompanied by noise, and its spatial and temporal characteristics can vary significantly, presenting challenges for model training. Moreover, effective preprocessing of the dataset is crucial. To address these issues, this paper proposes a neural network method based on orthogonal basis function (OBF) feature detection. This method combines OBF preprocessing with a combined training approach using CNN and long short-term memory networks, which effectively reduces the false alarm rate commonly associated with traditional methods. Consequently, the detection rate is substantially improved, leading to enhanced overall detection performance for magnetic anomaly signals.