# An Aeromagnetic Compensation Algorithm Based on a Residual Neural Network
###### Abstract  
Aeromagnetic compensation is a crucial step in the processing of aeromagnetic data. The aeromagnetic compensation method based on the linear regression model has poorer fitting capacity than the neural network aeromagnetic compensation algorithm. The existing gradient updating neural network-based aeromagnetic compensation algorithm is subject to the problem that the gradient disappears during the backpropagation process, resulting in poor fitting ability and affecting aeromagnetic compensation accuracy. In this paper, we propose a neural network compensation algorithm with strong fitting ability: residual backpropagation neural network (Res-bp). The algorithm realizes the cross-layer propagation of the gradient through a residual connection so that the network not only preserves the original information but also acquires additional information during training, successfully solving the problem of gradient disappearance and boosting the network's fitting capacity. The algorithm is applied to the data collected by unmanned aerial vehicles (UAVs) to verify its effectiveness. The results show that the improvement ratio is improved compared with the traditional neural network, demonstrating that the algorithm has a significant compensation effect on aeromagnetic interference and improves the quality of aeromagnetic data. a 20221210759  
tail-boom swing [10]. [PERSON] established a binary classification network for magnetic anomaly detection and a regression network for geomagnetic noise suppression and applied deep learning to magnetic anomaly detection and noise elimination [11]. However, in the backpropagation process of neural networks, owing to factors such as the chain rule, the gradient will disappear, affecting the accuracy of aeromagnetic compensation. [PERSON] used an autoencoder network for aeromagnetic compensation to extract data features by searching the direction of maximum change in the backpropagation process, thereby reducing the multicollinearity of the model. However, in the training process of the autoencoder network, the feature extraction of high-dimensional complex data is not perfect, the gradient is prone to fall into the local minimum, and the problem of gradient disappearance is not considered [12]. [PERSON] considered the gradient disappearance problem in the autoencoder network training and introduced a residual connection into the autoencoder network. The features output from each layer of the encoder are transmitted to the corresponding layer of the decoder through residual connections. The algorithm is applied to fault diagnosis of generator sets [13]. [PERSON] combined a linear model and neural network model and proposed a dual estimation method for aeromagnetic compensation [14]. Although this method can suppress the noise caused by the overfitting problem of the neural network, owing to the excessive number of iterations, the gradient will approach zero infinitely, and the gradient of the network will disappear, making it difficult to reduce the loss function, thus affecting the compensation effect. [PERSON] considered the main factors that affect the generalization ability of the network, analyzed the probability density function that constitutes the regression neural network, and cited the fully connected neural network for aeromagnetic compensation, which solved the problem of overfitting the network and improved the generalization ability of the network, although the process did not consider the problem of gradient disappearance [15]. To address the gradient disappearance problem of neural networks in aeromagnetic compensation algorithms, in this paper, we combine a residual connection with neural networks and propose a neural network aeromagnetic compensation algorithm (Res-Bp). The features output from the low-level information of the network are transmitted to the high-level information through the residual connection without intermediate operation to realize the cross-layer propagation of the gradient [16].