# An Aeromagnetic Compensation Algorithm Based on a Temporal Convolutional Network
## 1 Introduction  
Aeromagnetic surveys are widely used to aid in the production of geological maps and are also commonly used during mineral exploration and petroleum exploration. The signals collected during aeromagnetic surveys include various interferences, which affect the survey's accuracy. Therefore, it is necessary to study aeromagnetic compensation to eliminate aeromagnetic interference. In the early 1950s, [PERSON] and [PERSON] first analyzed the main sources of magnetic interference on flight platforms [1, 2, 3, 4], including those from intrinsic magnetic fields, induced magnetic fields, and eddy-current magnetic fields. In 1961, [PERSON] further proposed a mathematical model for describing aeromagnetic interference: the Tolles-Lawson model (T-L model) [5]. In 1980, [PERSON] described the solution of the T-L model as a linear regression problem and proposed a ridge regression method, which reduces the multicollinearity of the solving process [6]. In 2022, [PERSON] proposed a compensation method to resist the interference of geomagnetic gradients in aeromagnetic surveys [7]. In 2023, [PERSON] proposed a joint compensation method based on an inertial navigation system and a three-axis fluxgate magnetometer to improve the compensation accuracy [8] and then proposed a compensation method for airborne electronic equipment interference [9]. Considering the insufficient accuracy of traditional linear regression methods, more researchers have started studying neural-network-based aeromagnetic compensation methods. In 1993, [PERSON] first used neural networks for aeromagnetic compensation andtested the effects of fully connected neural networks at different layers. The results showed that the neural network compensation method had a similar accuracy to the T-L model compensation method and could be used as an alternative [10]. In the study on maneuvering interference and low-frequency interference compensation methods (usually within 0.1-0.6 Hz frequency [11]), [PERSON] proposed a dual estimation method based on an unscented Kalman filter in 2017, which combines the T-L model with a neural network to improve the compensation accuracy, and noise was added manually to solve the overfitting problem of neural networks [12]. In 2020, [PERSON] proposed an end-to-end neural network model for magnetic anomaly detection and aeromagnetic compensation. The model consisted of two parts: a binary classification network for magnetic anomaly detection and a regression network for geomagnetic noise suppression [13]. In 2021, [PERSON] proposed a neural network compensation method based on a generalized regression neural network to solve the overfitting problem of neural networks [14]. In the same year, [PERSON] designed a neural network compensation method based on physical features with the Levenberg-Marquardt backpropagation algorithm, which displayed an improved compensation accuracy [15]. In 2022, [PERSON] proposed a neural network compensation method that combines a backpropagation neural network and a residual connection to address the vanishing-gradient problem during neural network training [16]. In the same year, [PERSON] proposed a compensation method based on a radial basis function artificial neural network, which uses the radial basis function as the activation function to improve the neural network's generalization [17]. [PERSON] proposed a model compression method to optimize the backpropagation neural network to solve the problem of insufficient computing resources during the training process on unmanned aerial vehicles [18]. In 2024, [PERSON] proposed an incremental learning neural network compensation method that reduces the time required for neural network retraining [19].