# Deep Learning Applications in Geosciences: Insights into Technological Analysis
## 3 Results and Discussion
### Experimental Results 2: BI 0 vs. BI 1-2 vs. BI 3-6  
Our second experiment (EXP#2) is to test whether we can construct a 3-class bioturbation index classification using the same deep learning model. In this experiment, all images were grouped into three categories: (1) unbioturbated (BI 0), (2) moderately bioturbated (BI 1-2), and (3) intensely bioturbated (BI 3-6). In other words, we divide BI 1-6 images used in EXP#1 into two classes as BI 1-2 and BI 3-6 (Figure 3). We run the algorithm on test data set of 106 BI 0, 73 BI 1-2, and 83 BI 3-6 images. Again, we train our algorithm ten times, starting with different initializations for the trainable parts of the model, and test them on the same test data. The lowest accuracy we obtain is 85.1%, whereas the highest is 88.9%, with an average of 86.8% for the three-class classification problem (Table 2 and Figure 5). Based on the highest test accuracy results (88.9%), the majority of the BI 0 images are correctly classified, similar to the results of EXP#1 (Tables 1 and 2). Given that the BI 0 data set is identical in both experiments, correctly identified images are also similar. Therefore, 94.3% of the BI 0 images were correctly classified (Table 2 and Figure 5). However, EXP#2 showed slightly different accuracy results when the BI 1-6 class was divided into BI 1-2 and BI 3-6 classes. Among the 73 BI 1-2 test images, 84.9% are correctly classified. From the 83 BI 3-6 test images, 85.5% are correctly classified. Only 5.7% of the BI 0 images are misclassified in EXP#2, with most of these either identical to or similar to the images that are misclassified in EXP#1. EXP#2 shows lower precision in BI 1-2 images, with 15.1% misclassified. The vast majority of these misclassified images are labeled as BI 0 in our original classification. These images show very low bioturbation indices (the lower range of BI 1), large concretions, and single isolated trace fossils. For example, two misclassified images display diminutive escape structures expressed only by minor disruptions of laminae (Figure 6A), unlike the more pronounced ones included in the training data set. One image shows a large-scale escape structure but also includes some mineral formations or possible smeared burrows (e.g., Cylindrichnus) (Figure 6B). Precision in predicting BI 3-6 images is slightly higher compared to BI 1-2 images, with only 14.5% misclassified. The majority of these misclassified images occur at \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Experiment \#2**} & \\ \hline  & **Total Number** & **Number of** & **Number of** & & \\  & **of Images** & **Training Images** & **Test Images** & & \\ \hline BI 0 & 530 & 424 & 106 & 100 (94.3\%) & 6 (5.7\%) \\ BI 1–2 & 360 & 287 & 73 & 62 (84.9\%) & 11 (15.1\%) \\ BI 3–6 & 413 & 330 & 83 & 71 (85.5\%) & 12 (14.5\%) \\ Total & 1303 & 1041 & 262 & 233 (88.9\%) & 29 (11.1\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of experiment #2. Figure 4: Representative misclassified images in EXP#1: (**A**) image showing a deformed mud-drape (white arrow). The deformation caused the mud-drape to resemble bioturbation labeled as BI 1-2 present in some of the other images in the training images. (**B**) Individual _Ophiomorpha_, which is misclassified owing to the lack of similar images in the training dataset. the lower limits of BI 3 (e.g., approximately 31-45%) and are thus intergradational with the upper limits of BI 2. It should be noted that after EXP#1, but before the final results of EXP#2 were obtained, we used 877 training images for both experiments. Although initial EXP#1 results showed high accuracies, initial EXP#2 results showed considerably lower accuracies than the final EXP#2 results explained above. This issue was the lack of statistical diversity of the training dataset after we divided BI 1-6 images into two subclasses in EXP#2. To eliminate this issue, we added additional 82 images into the BI 1-2 class and 82 images into BI 3-6 class and ran the algorithm 10 times with different seeds.