## 5 Neural Network Computation  
After the selection process of seismic attributes, both supervised and unsupervised neural networks can be used to evaluate discontinuities [11]. In the current study, UVQ was used because the TFL and fracture density identified the most likely fault and fracture features. Therefore, the ANN was computed using the unsupervised algorithm. The fracture positions were connected with bed terminations that were categorized by maximum variation of dip values, maximum curvature attributes, low values of similarity, maximum values of TFL, and maximum values of fracture density and fracture proximity. Henceforth, those seismic attributes that were already shortlisted in the current study (such as polar-dip, FEF-similarity, TFL, maximum-curvature, fracture density, and fracture proximity) were chosen for neural network computation. The shortlisted seismic attributes were tested for input, and, after various quality checks (QC's), they went through a training program for ANN computation by using the UVQ of ten classes to yield an optimal output. The neural network tried to cluster the set of vectors, and similar vectors went into the same segment. Segmentation was performed using the shortlisted multi-attributes, and class centers were analyzed. The ANN-UVQ process comprised three distinct layers: the input, hidden layer, and output layers. These layers were connected with one another to make an entirely connected UVQ network that was comprised of ten nodes. Among them, the input layer consisted of six nodes (three related to the hidden layer), and the output layer was represented by one node. (Figure 11). The number of vectors assigned to each node and their average match to the corresponding class center were also analyzed, appearing as a robust classification. The input test data and the training data were applied in training, and this was continued until a straight line was obtained (that resulted above 75%), which suggested minimum error. The training was then ceased, and the results of the network were analyzed regarding the volume, section and time slices. The relative contribution of the seismic attributes UVQ classification is shown in Table 1. \begin{table}
\begin{tabular}{c c} \hline \hline
**Attributes** & **Relative Contribution** \\ \hline Polar-dip & 34.2 \\ Fault-enhancement Similarity & 31.9 \\ Thinned-fault likelihood & 95.4 \\ Maximum-curvature & 50.3 \\ Fracture-density & 100.0 \\ Fracture-proximity & 70.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Sensitivity table illustrating the input node relative weights of every attribute used for the ANN–UVQ. Figure 10: (**a**) Fracture proximity cube; (**b**) fracture density attribute cube. The yellow arrows show the regions of maximum fracture activity. The results of the ANN-UVQ highlighted the fracture network using the weighted average of all the selected attributes. The seismic cube and the seismic section of the ANN-UVQ showed the discontinuities with maximum probability, as shown by the red rectangles in Figure 12a,b. The maximum likelihood of fractures was also computed at different slices, which indicated that maximum fractures were present at slice 700 ms. In contrast, the probability of fractures at 2180 ms was minimum (Figure 13a-d). Since the reservoir in the study area lies from 2100 to 2400 ms. The likelihood of the fractures was also analyzed at 2250, 2300, 2350, and 2400 ms, thus giving the idea of the trend of the fractures in the study area (Figure 13e,f). These slices could be further exploited for future drilling prospects, if further evaluated. Figure 11: The ANN–UVQ (unsupervised vector quantization) network that was comprised of three layers of 10 nodes. The input layer had 6 nodes, the hidden layer had 3 nodes, and the output layer had 1 node. The colored legend shows the relative importance of every shortlisted attribute as an input to compute an output. Figure 12: The results of the neural networks highlighting the fracture network: (**a**) ANN–UVQ cube and (**b**) ANN–UVQ section.