Weak Magnetic Anomaly Detection Method Using ResNet-GRU Neural Network
In low signal-to-noise ratio(SNR)situations,magnetic anomaly generated by magnetic target is usually buried in the magnetic noise,leading to a decline in the detection performance of traditional magnetic anomaly methods.To improve the detection performance of weak magnetic anomaly under low SNR,a weak magnetic anomaly detection method using ResNet-GRU network is presented in this paper.In this method,the Conv1D modules based on ResNet and the GRU modules are employed to extract multidimensional features from magnetic anomaly signals,enabling the detection of such signals through the fusion of multiple features.To train the model,a real-world magnetic anomaly dataset is constructed,consisting of 8646 positive samples and 8431 negative samples.Experimental results demonstrate that proposed method using ResNet-GRU has an accuracy of 90.39%,a precision of 91.33%,and an F1 score of 90.18%on the test set,out-performing the performance of fully connected neural network model and one-dimensional convolutional neural network mod-el.The proposed method has good detection performance of weak magnetic anomaly under low SNR.
magnetic anomaly detection(MAD)deep learningfeature extractiongated recurrent unit(GRU)
NETL
NSTL
万方数据
