# Deep Learning Logging Sedimentary Microfacies via Improved U-Net
## 3 Results and Analysis
### Result Analysis
The confusion matrix of the prediction results for various methods is shown in Figure 11. It can be seen that the ANN and SVM methods have inaccurate feature extraction for sedimentary microfacies and are prone to misclassification, while Raw U-net and Improved U-net show relatively good classification results. Compared with Raw U-net, Improved \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**S/N** & **Data without Noise** & **12** & **10** & **8** & **5** & **3** & **2** \\ \hline Improved U-net & 86.49\% & 78.36\% & 72.68\% & 62.44\% & 36.43\% & 10.53\% & 11.23\% \\ Raw U-net & 81.53\% & 70.46\% & 71.34\% & 54.95\% & 22.35\% & 12.61\% & 9.65\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The accuracy rate of the network model under different SNR. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Input DataClassification Model** & **SVM** & **ANN** & **Raw U-Net** & **Improved U-Net** \\ \hline DEN, GR, SP, L & 60.26\% & 65.20\% & 69.47\% & 73.34\% \\ DEN, DEN-D, DEN-G, DEN-A & 48.04\% & 49.83\% & 58.95\% & 64.71\% \\ GR, GR-D, GR-G, GR-A & 55.11\% & 60.63\% & 68.12\% & 74.31\% \\ SP, SP-D, SP-G, SP-A & 55.11\% & 58.65\% & 63.52\% & 68.09\% \\ \hline GR, GR-D, GR-G, GR-A, DEN, & & & & \\ DEN-D, DEN-G, DEN-A & 62.85\% & 60.30\% & 76.59\% & 80.93\% \\ SP, SP-D, SP-G, SP-A, DEN, & & & & \\ DEN-D, DEN-G, DEN-A & 63.01\% & 63.18\% & 76.13\% & 78.88\% \\ SP, SP-D, SP-G, SP-A, GR, & & & & \\ GR-D, GR-G, GR-A & & & & & \\ DEN-D, DEN-G, DEN-A, GR, & & & & \\ GR-D, GR-G, GR-A, SP, & & & & \\ SP-D, SP-G, SP-A & & & & & \\ \hline \hline \end{tabular}
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Table 3: The accuracy obtained by the four models on eight test datasets with different input data characteristics. U-net can be used to more accurately characterize submarine distributary channels and the river channel bay, but the identification of the river channel margin is not as good. This may result in the misclassification of sandstone interbeds at the edge of the river channel as distributary channels mainly composed of sandstone, and the misclassification of mudstone interbeds as river channel bay mainly composed of mudstone. Figure 11: Confusion matrix of the prediction results (**a**) ANN; (**b**) SVM; (**c**) Raw U-net; (**d**) Improved U-net. Figure 10: Variation in accuracy with sample size under different network architectures. The segmentation and identification results of the test wells is shown in Figure 12. The experimental results show that our method can be applied to the description of sedimentary microfacies in well logging data. Compared with the manually identified original labels, this method can accurately segment different categories of sedimentary microfacies and identify them. For the results in Figure 12a,b, the segmentation results of the ANN and SVM methods show fragmented characteristics, which are due to the fact that the input samples only consider the well logging curve values at the depth points without considering the morphological changes of the well logging curves. On the other hand, both Raw U-net and Improved U-net segmentation results perform better, with good continuity and block. Most of all, Improved U-net shows the best performance. In local areas, due to sudden changes in the structure of well logging curves, Improved U-net can perform more detailed identification of sedimentary microfacies. Compared with manual identification, this method has higher sensitivity and can further refine the results of manual classification. Figure 12: The testing results of different methods for Triassic formation in the LN area of Northern Tarim Basin, 1, 2 and 3 represent three sedimentary microfacies: submerged bifurcated channel, river channel margin and river channel bend, respectively; (**a**) depth range from 4500 m to 4700 m; (**b**) depth range from 4700 m to 4880 m.  
The lithologic profile of the test well is shown in Figure 13, along with a comparison of the results obtained using our method and geological experts (labels) [30].