# A Prediction Model of Marine Geomagnetic Diurnal Variation Using Machine Learning
## 3 Overview of the Study Area and Data Processing
### Data Preprocessing  
In order to ensure the integrity of the experiment and mitigate the impact of outliers and missing data in the geomagnetic station dataset, the Grubbs outlier detection method is first employed for detection and removal. Jump points and missing values are subsequently addressed through conformal cubic interpolation. When applying the SVM-RF model for estimating daily variations, a specific geomagnetic station is selected as the main station, with the remaining stations utilized as training stations for model training. In this study, the daily variation base value is determined using the base period method. Specifically, the mean value derived from geomagnetic data collected during 7 h intervals from 21:00 to 24:00 and from 00:00 to 04:00 is selected as the daily variation base value. The daily variation base value signifies the stable magnetic field value at a station when unaffected by external magnetic field influences. Subtracting the daily variation base value from the observed geomagnetic station value yields the daily variation data [18]. In order to enhance the correlation of training data and mitigate the impact of time differences between the main station and training stations, it is essential to adjust the training station data to align with the location of the main station. Given the intricate nature of geomagnetic diurnal variation, this study proposes the use of an extreme value adjustment method to address the time difference. The extreme value adjustment method aligns the daily variation data of training stations with the main station by averaging the differences between the extreme value times of the main station and other training stations' local daily variation data. The selection of the time window should capture the representative extreme values of the time difference, particularly when the curve exhibits concave-convex property changes, as shown in the local area corresponding to \(\Delta T_{1}\) and \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**IAGA Code** & **Latitude (\(\lx@math@degree\)N)** & **Longitude (\(\lx@math@degree\)E)** & **Altitude (m)** & **Station** & \multicolumn{5}{c}{**BEL**} & **ESK** & **FUR** & **HAD** & **HLP** \\ \cline{5-10}  & & & & & & \multicolumn{3}{c}{**Distance (km)**} \\ \hline BEL & 51.836 & 20.789 & 180 & Belsk & 0 & 1622 & 792 & 3090 & 335 \\ ESK & 55.314 & 356.794 & 245 & Eskdalemur & 1622 & 0 & 1271 & 1491 & 1402 \\ FUR & 48.165 & 11.277 & 572 & Furstenfeldenbruck & 792 & 1271 & 0 & 2572 & 885 \\ HAD & 50.995 & 355.516 & 95 & Harland & 3090 & 1491 & 2572 & 0 & 2891 \\ HLP & 54.603 & 18.811 & 1 & Hel Observatory & 335 & 1402 & 885 & 2891 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Information of each station. Figure 4: Map depicting the distribution of geomagnetic station locations. \(\Delta T_{2}\) in Figure 5. The determination of the number of time windows is contingent upon the actual circumstances, typically exhibiting a positive correlation with the data's time span. \[\Delta T=\frac{\Delta T_{1}+\Delta T_{2}+\cdots\Delta T_{n}}{n} \tag{15}\]  
Equation (15) represents the calculation equation for the correction time in the extreme value adjustment method, where \(\Delta T_{1}\) is the time difference of the first local area, 'n' is the number of selected local areas, and \(\Delta T\) is the mean value of each time window as the final correction value. The schematic diagram illustrating the extreme value adjustment method is presented in Figure 5:  
The daily variation observation data from the ESK and FUR stations in Europe for the period of 15 to 17 November 2018 are depicted in Figure 5. Equation (6) is applied to calculate the time difference correction value using three selected time windows for adjustment. Figure 5a illustrates the sample data without time difference correction, while Figure 5b displays the data after extreme value adjustment correction. The main station is designated as station A, and station B is selected for time difference correction.