# A Stratigraphic Prediction Method Based on Machine Learning
## 3 Results and Discussions
### Machine Learning Simulation Result Analysis
#### 3.2.1 Training and Verification of the Stratum Type Series Model
The variation curve of the predicted series similarity with the number of training rounds is shown in Figure 13.  
Figure 11: Variation diagram of the simulation accuracy of the RNN model. Figure 12: Variation curve of the geostratigraphic series edit distance with time. In Figure 13, with an increase in training rounds, the overall prediction ability of the model is continuously improved, and the average similarity curve between the predicted series and the actual geoststratigraphic series also gradually increases and finally converges to 70.9%. This result shows that model accuracy continuously improves with increasing training rounds in the learning process and gradually establishes the correlation between the elevation information and the geost stratigraphic series in the study area. (3) Testing the Effect of Expert-Driven Learning  
To improve the learning performance of the RNN and test the effect of expert-driven learning, this study conducted the training and testing of the expert-driven model based on supervisory learning in accordance with four ratios using the same dataset. The four expert ratios are 1/3, 1/2, 2/3 and 1, i.e., expert-driven learning is carried out once every three rounds, once every two rounds, and twice every three rounds, and the entire training process is conducted in the form of expert-driven learning. Figures 14-17 show the loss function curves of expert-driven learning using different factors. Since the model is based on the prediction results of both expert-driven learning and non-expert-driven learning, the loss function is banded in the first three figures. The model obtained a higher descent gradient under the guidance of correct monitoring signals as compared with the ordinary RNN model. The larger the proportion of expert-driven learning in the learning process is, the higher the rate of loss reduction. When expert-driven learning is completely adopted, the model loss function curve decreases the fastest. Almost all of the gradient descent is completed within the first 50 training rounds. Figure 14: Expert-driven learning with a factor of 1/3. Figure 13: Similarity curve of the geost stratigraphic series. The statistics of series similarity under different expert ratios are shown in Table 5.  
It can be seen that adopting the expert-driven learning mechanism is helpful to improve the performance of test models for stratum type series simulation based on machine learning, as shown in Figure 10:0 However, the amplitude of the improvement effect is not significant. The expert-driven model can accelerate the convergence of the learning curve, and the higher the expert ratio is, the faster the model will reach stability. From the highest and stable values of the various indicators in the different models, it is not the rule that the higher the expert ratio is, the better the effect will be. The ultimate performance of full expert-driven learning was only slightly better than that of the RNN model. The best results were obtained by using a partial expert-driven learning strategy model.