# Deep Learning Applications in Geosciences: Insights into Technological Analysis
## 3 Results and Discussion
### Experimental Results 1: Unbioturbated versus Bioturbated Facies  
The first experiment (EXP#1) is for differentiating unbioturbated facies (BI 0) from bioturbated facies (BI 1-6) (Figure 3). To achieve this, we train our algorithm using BI 0 images and BI 1-6 images (Table 1). The algorithm ran ten times on a test data set comprising 106 BI 0 and 156 BI >0 test images (Table 1). Accuracy values range between  
Figure 2: A convolutional neural network architecture based on VGG-16 with fully connected layers at the end. The architecture describes the routines for both binary and multi-class classifications. The input is a three-channel (red, green, blue) image. The binary classification problem outputs whether the input image is bioturbated or not. The multi-class classification problem outputs the class of the given image in terms of its bioturbation index.  
93.8% and 97.7%, with an average accuracy value of 95.9%. The analysis below represents the highest accuracy results.  
Among BI 0 and BI 1-6 test images, 98.1% and 97.4% are correctly classified, respectively. Complex primary structures (e.g., hummocky cross-stratification and amalgamated current ripples) and several non-biogenic structures that resemble biogenic features (e.g., mudstone rip-up clasts, concretions, gravel lags, scattered coal fragments, flame structures, fractures/cracks, and soft-sediment deformation) are correctly classified or did not cause confusion for the network. The algorithm also successfully classified outcrop images along with core images and facies characterized by various grain sizes (e.g., fine-grained sand to gravel), and successfully ignored human-made features such as pen marks and surface stains.  
In order to better understand our results and improve our algorithm for future applications, we also analyzed the misclassified images and compared them with similar images in the training data set. Only 1.9% of the BI 0 test images are misclassified as BI 1-6, and 2.6% of the BI 1-6 test images are misclassified as BI 0 (Table 1). These misclassified images display either rare, deformed mud drapes resembling bioturbation (e.g., Figure 4A), extremely diminutive trace fossils, individual ichnogenera for which there are a few or no examples in the training data set (Figure 4B), or sediment-swimming structures for which there are no images in the training data set.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Experiment \#1**} \\ \hline  & \multicolumn{1}{c}{**Total Number**} & \multicolumn{1}{c}{**Number of**} & \multicolumn{1}{c}{**Number of**} & \multicolumn{1}{c}{**Accurate**} & \multicolumn{1}{c}{**Misclassified**} \\  & **of Images** & **Training Images** & **Test Images** & & & \\ \hline BI 0 & 530 & 424 & 106 & 104 (98.1\%) & 2 (1.9\%) \\ BI 1–6 & 773 & 617 & 156 & 152 (97.4\%) & 4 (2.6\%) \\ Total & 1303 & 1041 & 262 & 256 (97.7\%) & 6 (2.3\%) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of experiment #1.  
Figure 3: Examples of the expert-labeled training images used in our algorithm. Overall, 1303 core and outcrop images are labeled based on their bioturbation indices.