# A Stratigraphic Prediction Method Based on Machine Learning
## 3 Results and Discussions
### Three-Dimensional Geological Modeling and Testing
#### 3.3.1 Three-Dimensional Geological Modeling
\begin{table}
\begin{tabular}{c l l l l} \hline \hline \multirow{2}{*}{**Number**} & \multicolumn{2}{c}{**The Real borehole Strata**} & \multicolumn{2}{c}{**Prediction Results of 3D Geological Modeling**} \\ \cline{2-5}  & \multicolumn{1}{c}{**Statum Type Sequence**} & \multicolumn{1}{c}{**Statum Thickness Sequence**} & \multicolumn{1}{c}{**Statum Type Sequence**} & \multicolumn{1}{c}{**Statum Thickness Sequence**} \\  & \multicolumn{1}{c}{**Statum Type Sequence**} & \multicolumn{1}{c}{**(m)**} & \multicolumn{1}{c}{**(m)**} & \multicolumn{1}{c}{**(m)**} \\ \hline
1 & slit, clay & 0.3,39 & clay, slit & 0.3,3.9 \\
2 & clay & 2 & miscellaneous fill & 2.0 \\
3 & miscellaneous fill & 0.6 & miscellaneous fill & 0.6 \\
4 & plain fill, clay & 3.1,98 & miscellaneous fill & 13.5 \\
5 & miscellaneous fill, clay, mudky soil, plain fill, clay & 12. 13, 15.5, 24, 13.3 & miscellaneous fill, clay, mudky soil, slit & 12. 13, 39, 13.3 \\
6 & flours soil, silly clay, plain fill, clay plain fill, clay & 10.0,5.2, 12.0, 3.3, 6 plain fill, sil, clay, silt & 1,0.5, 2.5, 12.2, 3.9 \\
7 & miscellaneous fill, plain fill, clay & 0.7, 30.4, 45 & miscellaneous fill, silt & 0.7, 8.5 \\
8 & miscellaneous fill, clay & 0.6,40 & miscellaneous fill & 4.6 \\
9 & miscellaneous fill, plain fill, clay & 0.5, 10.19 & miscellaneous fill, silt & 0.5, 0.5 \\
10 & miscellaneous fill, clay & 1.0, 98 & miscellaneous fill & 12.2 \\
11 & miscellaneous fill, slit, plain fill, clay & 4.1,11.2, 7.0, 10.0 & miscellaneous fill, silt & 28.25, 2.2 \\
12 & flours soil, plain fill, mudky soil, clay & 0.5, 67.2, 12.8, 6 plain fill, silt & 0.5, 16.5 \\
13 & silt, clay & 0.4, 6.6 & plain fill & 7 \\
14 & slit, clay & 0.4, 10.4 & plain fill & 10.9 \\
15 & miscellaneous fill, sil, plain fill, clay & 0.7, 19.3, 34.24, 20 & miscellaneous fill, plain fill, silt & 0.7, 19.3, 34.24 \\
16 & miscellaneous fill soil, plain fill soil, old city & 1.2, 26.6, 65.13, 0 & miscellaneous fill, plain fill, old city & 1.2, 2.6, 22.5 \\
17 & miscellaneous fill, solid, plain fill soil, clay & 0.5, 28, 10.2 & miscellaneous fill, plain fill & 0.5, 13 \\
18 & miscellaneous fill soil, plain fill soil, clay & 2.1, 08, 12.9 & miscellaneous fill, silt, clay & 2.1, 0.8, 12.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of the real borehole stratum conditions and 3D geological modeling prediction results. From Table 10, the 3D geological model performs poorly in terms of the number of layers, stratum type, and sequence similarity, but it can better predict the stratum thickness. When the prediction of the stratum type is accurate, the corresponding thickness prediction is close to the real value. Some borehole data are randomly selected in the training set, and the borehole coordinate information is input into the 3D geological model to obtain the stratum sequence prediction results of the borehole points. According to the statistics, in all the data of the test set, the 3D geological model accurately simulates 30.78% of the stratum types, and the similarity between the simulated series and the real geoststratigraphic series is 32.27%. In addition, the accuracy rate of the stratum thickness is 64.52%, as shown in Table 11.  
Comparing Tables 9 and 11, the prediction results histogram of machine learning and 3D geological modeling is obtained in terms of the stratum type, average series similarity, and stratum thickness accuracy, as shown in Figure 42.  
Figure 42 shows that there is a certain difference in accuracy between the geost stratigraphic series models based on 3D geological modeling and machine learning. Generally, these two methods can describe the real stratum situation well. The model based on machine learning has a good simulation effect in terms of the stratum type, and all its corresponding indexes are superior to those of the traditional 3D geological model. The machine learning model provides stratum information by predicting the layer thicknesses within the strata and it is slightly more accurate than the 3D geological model.