## 3 Results
### Automatic Fault Extraction  
To build an accurate geological model, it is of prime importance to understand the structural framework of the subsurface. Traditionally, this has been accomplished by manually picking the faults identified on the seismic dataset, which is an inefficient and time-consuming process. It is even more so with poor quality seismic data. In most cases, fault interpretation is also influenced by the predefined bias of the interpreter. By utilizing the modern tools and techniques, Geoscientists today can interpret virtually all seismic discontinuities in a seismic volume that are difficult to pick otherwise. Automated fault extraction enables the seismic interpreter to generate an initial fault network from 3D seismic data with very little manual intervention. The interpreted faults can be edited later in a much shorter time in an efficient manner. Consequently, this renders much-needed time to the interpreter for other prospect generation activities. Efforts in automatic fault detection and extraction has accelerated during the last two decades. Several seismic attributes have been developed, such as coherency, variance and chaos, which have been used to capture the fault locations, but with limited success due to seismic signal heterogeneities in the vicinity of the faults affecting the attribute values [39]. More recently, machine learning techniques have grabbed considerable attention in this direction, but these require a lot of data to train the software. Huge datasets are not readily available to everyone; hence machine learning techniques may not produce viable results in the foreseeable future. Keeping this context in view, methods based on structure-oriented semblance [40] have been more successful and produced the best results to date. Fault Likelihood is one of these methods which was developed by [PERSON] in 2013. Most of the available methods are based either on seismic continuity or discontinuity; however, [PERSON]'s methodology is based on fault-oriented semblance attributes, which makes it superior. The method supports manual, semi-automated based on seed faults and fully automated fault interpretation. The tracking algorithm is designed to calculate the fault dips and strikes constrained by predefined ranges to maximize the fault likelihood across the seismic volume. The input parameters are crucial to the procedure and depend on how seismic reflectors are dipping. If the geological scenario is simply layer-cake-like, small-angle ranges and fewer scan iterations may provide good results. Wider dip and strike ranges are recommended if detailed information is intended. Here, we have used the angle \(\theta\) to scan the fault likelihood frequencies (f):  
\[Fault\;dip\;angle=\theta\]  
\[Highly\;Dipping\;fault\;90^{o}or\;Verti\;cal\;fault=\theta=0\]  
After the suitable parameter selection was made, an automatic fault extraction algorithm was run on fault-tracking volume (TFL+Ant-tracking combined) to generate fault patches. More than 500 major and minor fault patches were extracted from the fault-tracking volume, as shown in Figure 7a. Ant-Tracking Computation  
Ant-tracking is a superior algorithm that adds further value to the workflow. It boosts not only major and minor discontinuities but also offers automated fault extraction and useful fault analysis tools. The algorithm uses the principles from ant colony systems to extract surfaces appearing like trends in very noisy data. Intelligent software agents called ants try to extract features from the data whose behavior corresponds to a fault. If many ants pick up a certain behavior or trend, it is extracted. Whereas noise and the remains of reflectors should be extracted by no ants or by only a single ant, in which case they will be deleted. The approach is fully 3D and can take full advantage of surface information in the neighboring voxels. This makes it possible to derive detailed information from the data or an attribute.