# Research on 3D Geological Modeling Method Based on Deep Neural Networks for Drilling Data
## 5 Experiments and Results
### Validation and Results  
To enhance the performance of the deep neural network, as elucidated in Section 4.1, a common approach entails the optimization of both the network's architecture and the hyperparameter configurations for each subnetwork, followed by training the neural network using these parameter settings. A frequently employed strategy is the extension of the training epochs to facilitate the convergence of the network's loss function toward the global minimum. Nevertheless, an excessive increment in the number of training epochs not only imposes a substantial demand on computational resources but can also engender overfitting, thereby impairing the network's capacity for generalization. To address the overfitting challenge, this paper incorporates the early-stop mechanism, which is designed to thwart overfitting by halting the training process when the network approaches its optimal performance state. By specifying a tolerance threshold for the number of validation rounds, it preserves the network's opportunity to approach a more optimal state, even in the face of fluctuations or temporary stagnation. This approach aids in precisely identifying the appropriate juncture to terminate training, avoiding premature cessation while safeguarding against overfitting. The amalgamation of the early-stop mechanism and the parameter setting for the number of validation rounds, denoted as 'N', empowers deep neural networks to attain enhanced generalization performance, resulting in superior training outcomes. Tables 1 and 2 showcase the training parameters and outcomes of the deep neural network, respectively. The utilization of the parameters from Table 1 in training the deep neural networks leads to a gradual decline in training loss across all three subnetworks, ultimately stabilizing within the predetermined number of training iterations. Concurrently, the validation loss and test loss consistently exhibit a decreasing trajectory. These observations lend support to the effective convergence of the neural networks within their respective training rounds, alluding to their avoidance of overfitting. Subnetwork DNN1, tasked with predicting geological epochs, notably achieves an impressive accuracy of 97.25% on the test set. Subnetwork DNN2, responsible for predicting geological genesis, delivers a commendable accuracy of 90.18% on the test set. Subnetwork DNN3, designed to forecast geotechnical body types, attains a solid accuracy of 85.17% on the test set. Cumulatively, the networks demonstrate a robust capacity for accurate prediction, effectively capturing the geological characteristics of the study area. In summation, these results underscore the formidable performance of the established deep neural network during both the training and testing phases. The network consistently attains a high level of accuracy in predicting geological data, substantiating its efficacy and generalization capabilities. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Indicators**} & \multicolumn{3}{c}{**Subnetwork**} \\ \cline{2-4}  & **DNN-1** & **DNN-2** & **DNN-3** \\ \hline Duration (s) & 4813 & 2840 & 3114 \\ \hline Total epochs & 1305 & 752 & 809 \\ \hline Best test loss & 0.0239 & 0.4256 & 0.3131 \\ \hline Testing Accuracy (\%) & 97.25 & 90.18 & 85.17 \\ \hline Best training loss & 0.0099 & 0.1858 & 0.6158 \\ \hline Best validation loss & 0.0237 & 0.4202 & 0.5304 \\ \hline Final learning rate & 6.46108 \(\times\) 10\({}^{-5}\) & 2.05891 \(\times\) 10\({}^{-4}\) & 1.85302 \(\times\) 10\({}^{-4}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Deep neural network training parameters. To further evaluate the generalization capacity of the deep neural network, ten sets of original data have been chosen for each of the following drill depths: 30 m, 50 m, 70 m, and 90 m. These datasets were not utilized during any phase of the network's training, validation, or testing.