# An Aeromagnetic Compensation Algorithm Based on Radial Basis Function Artificial Neural Network
## 1 Introduction  
With the development of the global economy, the demand for mineral resources in all countries in the world is also increasing. However, due to complex terrain conditions, many areas rich in mineral resources cannot be explored. In order to increase the detection range and improve exploration efficiency, aeromagnetic measurement technology has been rapidly developed. Airborne magnetic surveying is an important airborne geophysical exploration method, which can be used for magnetic data acquisition under various complex terrain conditions. Moreover, UAV technology has developed very rapidly and has been well used in all walks of life, so UAV survey technology has gradually developed, and is now widely used in resource exploration, regional survey and other fields [1; 2]. With the development of UAV technology, more and more countries have carried out the research and development of UAV aeromagnetic measurement equipment technology and achieved remarkable results. The available information indicates that the first company in the world to develop UAV aeromagnetic survey equipment was Magsurvey in the United Kingdom, which developed the PrionUAV aeromagnetic survey system in 2003 [3]. Since then, many companies around the world have conducted research and development of UAV aeromagnetic survey systems, such as the GeoRanger-I of the Dutch company Fugro [4], the Canadian company Universal Wing Geophysical (UWG) Venturer [5], the Japanese RMAX-G1 [6], the Swiss and Germanjointly developed reconnaissance B1-100 [7], the German MD4-1000 [8], CH-3 [9] from the Institute of Geophysical and Geochemical Exploration (IGGE) of the Chinese Academy of Geological Science, and an integrated multi-rotor aeromagnetic survey system at Queen's University in Canada [10]. Due to the ferromagnetic material inside the UAV and its various influences during flight, it will inevitably cause certain interference to the data collected by the magnetometer sensor; if we want to obtain high-quality aeromagnetic data, we must study the appropriate aeromagnetic compensation technology [11; 12].The compensation methods of aeromagnetic interference are mainly divided into hardware compensation and software compensation. The hardware compensation method is to first calculate the magnetic interference of the detection platform, and then add several coils to the detection platform to counteract the magnetic interference generated by the aircraft. In the late 20 th century, high-cost, low-precision hardware compensation began to be slowly replaced by software compensation [11]. According to the nature and causes of magnetic interference, [PERSON] and [PERSON] divided it into constant interference, induced interference, and eddy current interference, and established the classic Tolles-Lawson model (T-L model) [13], which is the foundation on which current aeromagnetic compensation methods are built. Based on the T-L model, [PERSON] established an aeromagnetic compensation method based on FOM compensation flight [14]. The variables in the T-L equation are not independent of each other, and the linear relationship between the variables affects the stability of the solution, and the linear relationship between the variables is called multicollinearity. [PERSON] proposed a small-signal method to weaken the linear relationship between variables, resulting in a more stable solution [15]. [PERSON] first proposed to overcome the multicollinearity problem of equations by introducing regularization terms through the linear regression method [16], [PERSON] et al. proposed a compensation algorithm for total field gradients [17]. [PERSON] proposes a new real-time method based on recursive least squares, and the simulation results showed that the method has a good ability to compensate for magnetic interference caused by an aircraft and its maneuvering [18]. [PERSON] et al. use principal component analysis (PCA) to reduce the multicollinearity of the T-L model [19]. [PERSON] applied deep learning to magnetic anomaly detection and noise cancellation [20].