# Deep Learning Applications in Geosciences: Insights into Technological Analysis
## 2 Materials and Methods
### Network Architecture  
Among popular computer vision tasks, such as object detection, image segmentation, and image-resolution enhancement, image classification is one of the most addressed application areas using deep convolutional neural networks (DCNNs) [1; 2; 3; 4; 5]. DCNN is a class of deep networks formed by a series of interconnected neurons that has led to great achievements on image classification problems. For example, [PERSON] et al. [1] proposed AlexNet, a DCNN designed to classify images from the ImageNet dataset, which contains 1.2 million high-resolution human-annotated images with 1000 different classes. Later, [PERSON] and [PERSON] [4] introduced a deeper architecture for better classification accuracies for the ImageNet dataset. Their proposed architectures, namely the VGG networks (e.g., VGG-16 and VGG-19), could also be applied to other image recognition tasks. For the bioturbation classification problem, we propose to train a DCNN using BI data labeled by an experienced achologist. These deep networks contain a large number of trainable parameters requiring a huge amount of labeled data. To avoid the need for manually labeling a massive collection of training examples, we used transfer learning--a deep learning technique that relies on storing knowledge gained while solving one problem and then applying it to a different but related problem. Therefore, instead of training from scratch, we rely on the VGG-16 convolutional neural network (CNN) model [4] and use part of its learned parameters derived from the training on the ImageNet dataset. This approach allows us to gain the same high classification performance with a minimal amount of manual labeling required. Such an approach has been proven to be successful in prior studies on a diverse set of applications [9; 21; 22; 23; 24; 25]. Our DL approach belongs to the supervised learning class, which aims at learning a function from known input and output pairs (training data). The learned function is then used in the prediction stage to map an unseen input (image) to an output (bioturbation index class). Teaching a DCNN to learn this mapping involves data preparation, model definition, and training. We used a total of 1303 color images that were selected approximately between 4 to 8 cm in size in order to make our algorithm applicable to core-based studies. Our outcrop images were likewise cropped roughly in the size of core dimensions. As the default input size for the pre-trained model, we used 224 \(\times\) 224 pixels, and we resized our images to the same size while visually preserving original sedimentary and ichnological structures without distortion. We carefully identified and labeled bioturbation indices in 3 classes. Among the 1303 images, 530 of them belonged to BI 0, 360 of them represented BI 1-2, and 413 of them represented BI 3-6. From these images, 1041 were used for training (79.9% of the overall images) and the remaining 262 (20.1% of the overall images) were used as test data for the evaluation and prediction stages. Perhaps one of the most difficult tasks in such classifications is to identify images that encompass more than one facies possessing different bioturbation indices (e.g., Figure 1). For such cases, we took average BI values of each bed and their thicknesses into consideration, particularly for the ones that were very close to the lower-end or upper-end cut-off values. Between BI 1-2 and BI 3-6, 74 images revealed this issue, and their bioturbation indices were calculated using the following formula:  
\[\text{BI}_{\text{avg}}=\frac{\left(\text{T}_{\text{a}}\times\text{BI}_{\text{ a}}\right)+\left(\text{T}_{\text{b}}\times\text{BI}_{\text{b}}\right)}{\sum\text{T}}\]  
where \(\text{BI}_{\text{avg}}\) is the average bioturbation index, \(\text{Ta}\), b, is the thickness of the individual beds, \(\text{BI}_{\text{a}}\), b, is the bioturbation index of each bed, and \(\sum\)T is total thickness. We carried out two main experiments in this study.