# A Prediction Model of Marine Geomagnetic Diurnal Variation Using Machine Learning
## 4 Analysis of Daily Variation Law  
Solar activity, which generates a substantial amount of high-energy particles interacting with the Earth's outer space, is closely linked to the diurnal variation of the geomagnetic field. Consequently, these interactions lead to the movement of charged particles within the Earth's magnetosphere, giving rise to current systems in outer space. As a result of these processes, the disturbed magnetic field appears as short-term fluctuations that are observed on the ground [19].  
As depicted in Figure 9, the Earth is impacted by a significant influx of high-energy particles generated by solar activity. Owing to the presence of the Earth's magnetic field, the trajectories of these particles are bent and diverted away from the Earth [20]. The particles interacting with the Earth's magnetic field cause short-term effects. Solar activity's complexity and irregularity lead to the intricate and variable diurnal variations in the geomagnetic field.  
Figure 10 provides a visual representation of the geomagnetic diurnal variation model across a localized area in Europe on 10 November 2018, at nine specific time points. The white line in the figure corresponds to the prevailing trend line at that particular moment, while the dotted line illustrates the vertical gradient shift within the area under consideration. The complex and variable nature of the alteration trends throughout the day is evident in Figure 10. These fluctuations typically manifest along either the latitude or the longitude axis, although no consistent pattern emerges. Moreover, there is a noticeable inclination between the latitude and longitude directions at various time points. For example, in Figure 10a, the gradient shift is observed toward the northwest. By 8 o'clock (Figure 10c), the gradient predominantly shifts to the northeast, with a minor segment on the right side indicating a northwestward trend, suggesting a gradual evolution in the gradient's orientation. At noon (Figure 10d), there is a shift in the gradient direction toward the northwest, followed by a transition to the northeast between 12 o'clock and 14 o'clock. Subsequently, from 16:00 to 20:00, the gradient direction reverts back to the northwest before changing again to the northeast by midnight. This continuous shifting of the geomagnetic diurnal variation pattern over the course of a day underscores a nonlinear trend that deviates from a strictly latitude- or longitude-based orientation.  
Figure 9: Diagram illustrating the Sun–Earth electromagnetic effects.  
Introducing machine learning to model the geomagnetic diurnal variation at each site is a valuable approach due to the dynamic and changing relationship between various geomagnetic observation sites over time. A single fixed function model may not accurately capture the relative relationships of geomagnetic diurnal variations at different sites because of the complexity and variability of the Earth's magnetic field behavior. By leveraging machine learning techniques, it becomes possible to create more adaptable and precise models that can account for the evolving nature of geomagnetic data. This approach provides better insights into the diurnal variations at each specific site.