## 4 Results  
Three-dimensional seismic imaging does not usually offer sharp imaging of faults and is sometimes ambiguous regarding fault geometry and continuity. In Figure 5C, we present the results of our interpreted horizons alongside manually picked faults to delineate geological features on inline 2940. Figure 5D reveals the outcome of our CNN model on the same inline which displayed remarkable resemblances to the manually picked faults. Fault polygons were created manually after picking the horizon of the top and base of the Lower Plover Formation (LPF) and a Two-Way Time (TWT) structure map was generated from the picked horizons (Figure 6). From the structure map it is observed that most of the faults are trending in a NW to SE direction. The reservoir zone lies between 3250 ms and 3500 ms on average, but the northern block is down-faulted to about 3700 ms.  
A deep-learning (DL) automatic fault detection pre-trained CNN algorithm was also applied to same 3D. The DL-identified faults are compared to the conventionally picked faults and variance lineaments. Deep-Learning CNN fault identification is shown in Figure 7B at a time slice of 3464 ms. There were three fault interpretation cases observed and investigated on both variance and CNN automatic fault detection. Case 1 represents faults that are clearly visible in both variance and CNN, Case 2 represents faults that are clearly visible in CNN but not in variance, and lastly Case 3 represents faults that are visible in variance but not detected or identified on CNN. In the following sections, a few examples from each case are shown. To facilitate the comparisons, a time slice of 3464 ms from both variance (Figure 7A) and CNN cubes (Figure 7B) was selected due to its proximity to the horizon of the Top Plover Formation. A few 3D arbitrary seismic sections crossing some faults were selected to demonstrate the clarity of event terminations for fault detection. **Case 1. **_Faults clearly visible on both variance and CNN cubes._  
As expected, almost all faults fell in this category. A few examples were selected to be presented. In Figure 8, clear continuous N-S trending fault (\(F\)) is seen on CNN (E) whereas it is seen as two disconnected segments on variance. In the vertical sections the faults show clearly. Spatial fault continuity is not clear on the variance slice as compared to that of the CNN. Another 3D arbitrary seismic section that crosses the gap between the two variance fault segments clearly shows the event terminations. In Figure 9, a different area showing two faults detected on both variance and CNN automatic fault detection. On the vertical seismic section (a'-b'), F1 fault is clearly seen whereas F2 fault is not. It is observed that a fault detected on the CNN time slice (F2) is not observed on the vertical seismic section. On the other hand, that same fault (F2) is also not observed on the variance slice. This raises a question as to the validity of the detected lineament on the CNN slice. Faults detected on both variance cube and CNN model are shown in Figure 10. The lateral extent of the fault on the CNN slice is quite shorter than that seen on the variance slice, even though the fault is quite distinct on the vertical seismic section. **Case 2. **_Faults clearly visible on CNN but not on variance cubes._  
This case demonstrates the value of CNN-identified lineaments that are not detected by the variance. Figure 11 represents an example of a fault that is detected on both variance and CNN, but does not extend far enough on the variance, even though it is quite clear on the vertical seismic section (a'-b'). **Case 3. **_Faults clearly visible on variance but not on CNN cubes._  
In Figure 12, the fault under consideration is seen on the variance cube but does not exist on the CNN slice. The vertical section supports the existence of the fault, which was missed on the CNN slice. Figure 5: **(A)** Uninterpreted seismic inline 2940. (**B**) Base map showing location of displayed inline a-a\({}^{\prime}\) and red circles represent well locations.