# Deep Learning Logging Sedimentary Microfacies via Improved U-Net
## 3 Results and Analysis
### Result Analysis  
To validate the superiority of our proposed method over conventional methods, we conducted experiments on eight different input datasets and compared the performance of our Improved U-net model with U-net architecture, as well as the ANN and SVM models used in traditional methods for well log lithology identification. The results demonstrate that our Improved U-net model performs better in identifying sedimentary microfacies. The following results are based on test datasets that were not involved in the training process. Table 3 shows the accuracy obtained by the four models on eight test datasets with different input data characteristics. The eight datasets were mainly divided into four categories:  
Category 1: Consists of only three well log curves and one lithology curve;  
Category 2: Consists of one well log curve and its corresponding three extension curves (slope, azimuth, variance);  
Category 3: Consists of two well log curves and their corresponding three extension curves (slope, azimuth, variance);  
Category 4: Consists of three well log curves and their corresponding three extension curves (slope, azimuth, variance). \begin{table}
\begin{tabular}{c c c c c c c c} \hline Learning Rate & 10\(-\)1 & 10\(-\)2 & 10\(-\)3 & 5 \(\times\) 10\(-\)4 & 10\(-\)4 & 10\(-\)5 & 10\(-\)6 \\ Acc (\%) & 37.82 & 27.18 & 84.02 & 83.10 & 86.42 & 80.24 & 78.80 \\ \hline Batch size & 1 & 2 & 4 & 6 & 8 & 16 & 32 \\ Acc (\%) & 82.23 & 80.69 & 83.42 & 86.98 & 76.24 & 79.18 & 76.18 \\ \hline Downsampling Times & 1 & & 2 & & & 3 & \\ Acc (\%) & 73.95 & & 84.69 & & & 81.86 & \\ \hline \end{tabular}
\end{table} 
Table 2: The accuracy rate of the network model under different parameter configurations. Table 3 shows that our proposed method has significant advantages over traditional ANN and SVM classification algorithms in identifying sedimentary microfacies. Compared with the Raw U-net architecture, our Improved U-net model can significantly improve the accuracy of sedimentary microfacies identification. In addition, compared with the DEN curve, GR and SP curves make a greater contribution to the characterization of sedimentary microfacies in this study area, and adding data features through attribute extension can significantly improve the test results. Among the input data consisting of DEN, GR, SP, DEN-D, DEN-G, DEN-A, GR-D, GR-G, GR-A, SP-D, SP-G and SP-A, the Improved U-net model has the highest accuracy at 86.49%. Noise was added to the input data and the influence of data with different signal-to-noise ratios on the deposition level was analyzed. Table 4 shows the accuracy of input data with different SNR in Improved U-net and Raw U-net models, and the highest accuracy of data without added noise in the two models. It can be seen that the accuracy of Improved U-net is higher than that of Raw U-net when the SNR is the same. Moreover, the higher the signal-to-noise ratio of the input data, the higher the accuracy of the Improved U-net for deposition micro-recognition. We also analyzed the impact of sample size on different network architectures. For the eighth input dataset (composed of 12 curves: three well log curves and their corresponding slope, azimuth and variance extension curves), we used a training dataset of 1250 \(\times\) 800 \(\times\) 12 well log curves and randomly selected 30%, 50%, 70%, 90% and 100% of the sample size to train different network models. The same test dataset was employed to achieve the recognition accuracy of each trained model and analyze the training effect. The accuracy of Raw U-net slowly increases with the increase in sample size, and the performance is poor (Figure 10). In contrast, the Improved U-net model performs well overall and the accuracy gradually increases with the increase in sample size.