## 3 Results and Discussions  
### Friction Interpolators  
The interpolator evaluation process has two components: first, the RMSE of each interpolator was calculated to quantify the difference between the measured and the predicted, and the ability of each interpolator to capture the spatial pattern was then examined to verify the credibility of the obtained RMSE. A total of six interpolators were examined: OK, RK, RF, RFSI, RFOK, and RFSIOK. In addition to the required positional data, elevation and slope information were included to assist in the interpolation task. Each interpolator was evaluated using the ten generateddatasets with increasing separation distance. Their performances quantified by RMSE are depicted in Figure 6.  
According to Figure 6, interpolator accuracy decreases as the separation distance increases, which is expected due to the decrease in information available to the model to capture the true spatial variation. However, the amount by which interpolator accuracy decreased with increasing distance varied. OK and RK had the slowest rate of performance degradation, with interpolation error increasing linearly between 100 and 900 m. Similarly, RF and RFOK had a relatively gradual rate of change but had a steeper slope and more instances of sharp increases in error. The two remaining interpolators, the RFSI and RFSIOK, were the least stable. After the separation distance increased to above 200 m, significant drops in performance were observed. Between 200 and 600 m, the error increased tenfold, which was significantly larger than what was observed in the four other interpolators. Overall, RK and OK showed the least sensitivity to changes in separation distance, followed by RF and RKOK and then the RFSI and RFSIOK. In terms of interpolation accuracy, OK was observed to be the most accurate interpolator; it had the lowest error at all separation distances except 600 m. The next best performers were the RF models followed by the RFSI models, with the hybrid versions of these models having lower errors. These findings are somewhat contrary to what was found in previous studies, where RF, RFOK, and the RFSI produced higher accuracy than OK and RK, with RK performing better than OK [6]. The RF-based models' relatively poor performance could be attributed to differences in the validation approach and interpolation task. While previous studies used random sampling to divide the data into training and validation sets, we manually removed data to create equally spaced gaps. This may have increased the difficulty of the interpolation task because the validation points were further away from the locations with measurements. Another factor is the difference in the interpolation task. For variables such as solar flare and mud sea content, the interpolator uses measurements from all directions, whereas for friction, interpolation was limited to only two directions. Regarding the observation that OK outperformed RK, one possible explanation is that the implementation of a regression model was unnecessary; the dataset was already trend free, i.e., the additional step of trend removal did not improve performance since all it did was reposition the data. In addition to evaluating interpolator performance through RMSE, it is also vital to examine the shape of the interpolator predictions to ensure that the models are capturing the pattern found in the input dataset, rather than simply predicting a constant value at all  
Figure 6: Interpolator performance comparison. locations. From 100 to 300 m, there was virtually no difference between the six interpolators, as all could mimic the spatial pattern. Only after 300 m did the performance begin to diverge. The main difference was that RF and the RFSI began to lose their ability to capture the local minimums and maximums found in the spatial pattern, which worsened as the separation distance increased (Figure 7). In other words, the amount of variation in the interpolations decreased as the separation distance increased. This issue was much less prominent in OK and RK.