## 4 Comparison Study
### Deep Learning  
Over the years, ANN has been used for solving data pattern recognition and classification problems [45, 46]. ANN methods have a noticeable ability to construct a complex mapping between nonlinearly coupled input and output data. A deep neural network (DNN), also known as deep learning is an artificial neural network (ANN) having several hidden layers and that can solve problems more efficiently and find very complex relationships among the attributes using multiple layers in between the input and output layers. Several popular geophysical researchers suggest using deep learning [10, 18, 41]. For analysing how a deep learning performs, we used multi-layer perceptrons which is also known as \"feedforward neural networks\". Usually, the number of layers is two or three, but theoretically, there is no limit. Among the layers, there is an input layer, some hidden layers, and an output layer. Multi-layer perceptrons are often fully connected. This means that there is a connection from each perceptron in a specific layer to each perceptron in the next layer. Two important parameters are the optimizer and loss functions, which are interrelated to each other. In order to minimize the loss function, the optimizer updates the weight parameters. The loss function acts as the guide to the terrain, letting the optimizer know if it is moving towards the right direction to reach the bottom of the valley called a global minimum.  
To implement the deep learning module, DTr (as mentioned in Section 3.3.1) is used as the training dataset. However, DTst is divided into two equal subsets: the validation set (15% of the main dataset) and the testing set (15% of the main dataset). At the time of training a network with the training set, some adjustments of weights need to be done. To make sure that the model does not overfit, the validation set is used. Since our target is to solve a multiclass classification problem, the loss function that we used for the module is \"sparse categorical crossentropy\". We used RMSprop as the optimizer of the deep learning module which is a very popular and speedy optimizer [47]. To use the deep learning module, we used two hidden layers. Rectified linear activation function (ReLU) was been used for the input and the hidden layers, since ReLU has recently become the default activation function when developing most types of neural networks because of its computational simplicity with great efficiency [48]. The majority of the research that achieves state-of-the-art results usually uses a deep learning module with a ReLU activation function in the input and hidden layers [48]. For the output layer, we selected softmax as the activation function. The softmax activation function is used for building a multi-class classifier that solves the problem of assigning an instance to one class when the number of possible classes is more than two. Since in our experiment the number of independent variables is 10 and the number of prediction classes is 5, the number of neurons or units in the input and output layers are set to be 10 and 5 respectively. For each hidden layer 64 neurons are taken into consideration.  
Figure 5: Support Vector Machine scores for electrofacies classification.  
Figure 6 illustrates the deep learning model loss and accuracy. The classification results are given in Table 7.