# Inspiration for Seismic Diffraction Modelling, Separation, and Velocity in Depth Imaging
## 3 Results and Discussion
### Diffraction Separation and Imaging using Correct Velocity Model  
After careful consideration of the velocity analysis and stacking process (Figure 11a), the data are ready for DFF as an input. Figure 11b shows the separated diffraction with an enhanced amplitude, which allows the hyperbola to be observed more accurately. For quality control (QC) purposes, full-wave and diffraction difference sections are generated to ensure the accuracy of the algorithm, as shown in Figure 11c.  
We considered two methods to compare the results of imaging using different velocity modeling techniques. Figure 12a shows the image section using semblance velocity analysis, which was used because of uncertainties in picking the velocity, as this is an interactive process and the chance of error is greater. In this analysis, the three major faults are not imaged and interpretation is difficult. Further, the aim of migration was to delineate the reservoir below 2.5 km, which was not resolved because of velocity error.  
The second method used for migration is the hybrid travel time method, which incorporates both the eikonal solver and the paraxial raytracing algorithm to calculate the velocity. It also handles the shadow zone and multipathing effects in the data. Figure 12b is a migrated seismic section with an accurate velocity model, which enhances the resolution as well as the structural clarifications. Further, the three major faults are interpreted accurately, as the resolution of the data is quite good. One of the unconformities was produced because of erosion in the geological time scale change at the depth of 2.5 km. These types of structures make imaging more complex; for example, below the fracture the interval velocity and migration of data might be wrong. Using the hybrid travel time method provides accurate velocity determination for migration. The target of our imaging was the reservoir, which at  
Figure 11: (a) Common Mid Point (CMP) stacked section after careful processing, including sorting of the gather data from the CMP gather data, amplitude correction, and NMO correction. (b) Diffraction section after application of dip frequency filtering with high amplitude display. Diffracted events are enhanced. (c) Difference or reflection section, showing that consideration of the diffraction is quite important for successful subsurface imaging, especially for faults and fractures.  
2.5 km has an anticlinal structure. This was imaged properly, as shown in the interpreted section in Figure 12b.  
The amplitude spectrum of the data is generally used to predict reservoir productivity by seismic imaging, as shown in Figure 13a. his approach uses the data in the time domain, while the output is the amplitude spectrum using a Fourier transform. Furthermore, a frequency spectrum is plotted against the three data sets for quantitative evaluation: (a) input shot gather data; (b) diffraction data; (c) migrated data (Figure 13b). This demonstrates a low-frequency enhancement with higher amplitude (blue), which is very important for imaging. A targeted zone below 2.5 km is resolved and can be interpreted for structural clarifications and for further reservoir characterization.  
In addition to providing high-resolution seismic data and a better signal-to-noise ratio, velocity information about the subsurface provide critical results. For this reason, an extensive velocity analysis was performed to select and prove the best method of velocity estimation. The hybrid travel time equation can successfully assess velocity, especially in the shadow zone and multipathing areas, which was proven by the results.