# A Prediction Model of Marine Geomagnetic Diurnal Variation Using Machine Learning
## 1 Introduction  
Solar activity disrupts the Earth's magnetic field, resulting in short-term global changes. Geomagnetic matching navigation, serving as a crucial supplement to inertial navigation, provides exceptional concealment and resilience against interference, positioning it as the primary option in place of GNSS navigation following satellite rejection. Nevertheless, the effectiveness of geomagnetic matching navigation hinges on the availability of high-precision geomagnetic data. Particularly, accurate geomagnetic diurnal variation data play a significant role in enhancing the precision of marine magnetic measurements, underscoring the necessity for precise diurnal data. In marine magnetic measurement, obtaining the required geomagnetic diurnal variation is crucial. For regions close to the shore, setting up a diurnal variation station on land suffices. However, the challenge arises for areas located far from the shore, where a single station cannot provide the diurnal variation data needed for measurement. To address this issue, a multi-station daily variation observation model based on spatial position relationships is commonly employed. This model proves essential in calculating the daily variation data essential for marine magnetic measurement in distant areas. The complexity of geomagnetic daily variation data poses a significant challenge. Its intricate morphological structure makes accurate acquisition a difficult task. Relying solelyon geographical location relationships for projections becomes unreliable in such cases. As a result, specialized methodologies are required to ensure the accurate acquisition of geomagnetic data for marine magnetic measurements in remote offshore locations [1]. The time-varying nature of geomagnetic daily variation is complex [2], and the data between different stations have numerical differences such as phase difference, amplitude difference, and morphological difference [3]. The selection of the base value of the geomagnetic daily variation and the adjustment of the phase difference also affect the accuracy of the geomagnetic daily variation projection, as well as the preprocessing of the data [4]. [PERSON] [5] was the first to use the method of geographic interpolation of latitude and longitude to calculate the geomagnetic diurnal variation correction in the magnetic measurement of the British Isles. [PERSON] [6] found that a single station cannot effectively correct the far-distance measurement area when correcting the daily variation in magnetic measurement observation data. He proposed using the separation reconstruction method and the weighted average method, which have equivalent calculation accuracy, but the weighted average method is faster and more convenient. [PERSON] [7] found that the traditional multi-station daily variation correction was based on geographic coordinates. The geomagnetic coordinate fitting method was utilized to enhance the accuracy of multi-station daily variation correction. Researchers found that geomagnetic coordinates exhibit a stronger correlation with the intensity of the geomagnetic field. Nevertheless, this approach imposes strict demands on the network of daily variation stations. Addressing the limitations of the latitude weighting method, the distance weighting method, and the direct average method, [PERSON] [8] proposed a two-factor weighting method based on direction and distance. This method improved the calculation accuracy of the geomagnetic diurnal variation data by considering the spatial position and geometric distance of the geomagnetic station, alongside an analysis of the relationship between geomagnetic field intensity and latitude. [PERSON] [9] analyzed the geomagnetic diurnal variation data obtained from three-day synchronous measurements at the Shahe, Zhengding, Shangqiu, and Dongying stations. Three fitting methods for modeling geomagnetic diurnal variation in local areas were proposed: two-dimensional polynomial least squares, space-time fitting, and linear interpolation.