## 3 Results
### Automatic Fault Extraction
By writing the extracted surfaces back to a volume, we get what is referred to as an enhanced attribute, or an ant track cube. This cube contains only what is likely to be true fault information. Ant-tracking can extract faults from a pre-processed seismic volume or an attribute like a fault likelihood. An ant-tracking algorithm combined with a thinned fault likelihood attribute was used to extract numerous fault patches automatically. The patches can then be picked automatically or manually to merge into elementary fault planes. Since fault likelihood is a very sensitive process, it picks up all discontinuities regardless of their size. To get a clear picture and organization of the desired faults, we used different approaches. In the first one, fault directions were used as a constraint and azimuthal filters were applied during ant-tracking attribute generation. Azimuthal filters applied in east-west and north-south directions helped to identify the orientation of the faults. Figure 8 shows the ant-tracking volumes where Figure 8a shows east-west trending faults and Figure 8b depicts faults with a north-south trend. Faults based on their orientation were effectively separated and analyzed this way. The results show that most of the faults are east-west trending in the study area. Figure 7: (**a**) 3D view of all major and minor fault patches extracted automatically using the fault-tracking volume. The inset (**b**) shows a planner view of fault patches. Another useful technique is to co-render the seismic amplitude with the generated attributes. Figure 9 shows co-rendering of seismic amplitudes (greyscale), fault likelihood attributes (bright color) and thinned fault likelihood attributes (yellow intensity). The terminations line up to make a trend along the fault likelihood helping the interpreter to identify the fault surfaces. Figure 8: Time slice at 1550 ms from ant-tracking attribute volume (**a**) showing only east-west trending faults controlled by azimuthal filtering available in stereonet. (**b**) Only north-south trending faults were controlled by stereonet. Red and green pointers show the major faults. Figure 9: 3D view of co-rendering of seismic amplitude (greyscale), fault likelihood attribute (bright color), and thinned fault likelihood attribute (yellow intensity). The third approach to analyze the resultant discontinuities is to perform multiple iterations to generate a volume that eliminates minor discontinuities but keeps the major ones. Figure 10a shows thinned fault likelihood with all possible major and minor discontinuities, whereas Figure 10b shows the second iteration of the thinned fault likelihood with minor discontinuities removed, presenting a much cleaner result. Figure 10: (**a**) Thinned fault likelihood combined with ant-tracking attribute is showing all major and minor discontinuities. (**b**) Unwanted and minor discontinuities have been removed through an iterative process, presenting a much cleaner result; (**c**) shows ant-tracking of variance cube and compares the results of hybrid attributes (in **a,b**) used in this study to a standard edge detection attribute like variance. Red and green pointers mark the major faults on all images. Figure 10c shows the ant-tracking of variance cube. Figure 10a-c also provides an opportunity to compare the results of hybrid attributes used in this study and a standard edge detection attribute variance. It is evident that the chosen attributes have delineated razor-sharp discontinuities successfully.