# Using a Machine Learning Method to Predict the Penetration Depth of a Gravity Core
## 4 Results  
Figure 4 shows the gravity sampler penetration depth prediction results using the training and test sets of the established machine learning model. The results predicted by the model are in good agreement with the actual results in both the training and test sets. As shown in Figure 5, the prediction error is small, despite the significant difference in gravity sampler parameters between the data measured at sea and the physical model tests. The prediction accuracy statistics of the train and test sets are shown in Table 2. Both datasets showed promising results on all four statistical scales, and the training set results are slightly better than those of the test set.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & _EVS_a  & _MAE_b  & _MSE_c [FOOTNOTE:c]Footnote c: \(R^{2}\) \\ \hline Train set & 0.98 & 0.34 & 0.56 & 0.98 \\ Test set & 0.95 & 0.26 & 0.38 & 0.94 \\ \hline \end{tabular}
\end{table}
Table 2: Prediction accuracy statistics of train set and test set.  
Figure 4: Comparison of machine learning model predictions with an actual penetration depth of gravity samplers. (**a**) Comparison of predicted depth values with actual depth values for train data; (**b**) comparison of predicted depth values with actual depth values for test data. The blue points represent the laboratory data obtained from physical model tests. The green points represent field data obtained from sea.  
The absolute values of the error between predicted and real penetration depth are shown in Figure 4. The training set error of most cases is less than 1 m. There are six cases with error values between 1 and 2 m and one case with an error more than 3 m in the train set. As for the test set, 23 cases of 27 are less than 0.5 m, and there are only 2 cases with errors over 1 m. Many factors affect the penetration depth of gravity sampling (geology, marine environment, seafloor topography, etc.). Even if the same sampler is used under the same geological conditions, the depth of each sample is not the same, and the error can reach 2\(\sim\)3 m [9]. Therefore, numerical model prediction results within 2\(\sim\)3 m are acceptable. The machine learning model used in the paper is exceptionally accurate, as most errors are less than 1 m.  
After analyzing the data, we found that the 18 examples with large error values were all data measured at sea. There are two main reasons for this situation: (1) the amount of data of the same type and (2) the error of the data itself. When there are only 19 actual at-sea data point with a slight difference in sampler quality and 56 physical model test data, the model will tend toward the accuracy of the physical model test. Another reason is that human operation and recording errors of at-sea sampling are more significant than those in physical model tests. The high value of the prediction error for the offshore sampling depth is due to the typical machine learning model training problem caused by the relatively small amount of offshore sampling data. In the training process of machine learning models, the accuracy of the model can be increased when the training data sample is large enough. When the training data sample is insufficient, errors tend to be increased. The training data used in this paper are limited due to the difficulty of obtaining data for ocean gravity sampling. The model prediction error will decrease with increased offshore gravity sampling penetration depth data.