## 3 Methodology
### Seismic Conditioning  
It is necessary to apply various filters to obsolete the redundant noise while keeping geometrical features of seismic sections, because seismic attributes are susceptible to the noise within the seismic data [79]. For this purpose, multiple filters can be used to remove noise in seismic data; these filters are mean, median, and diffusion. The mean filter improves long-wavelength trends and reduces short-wavelength features. Moreover, it measures the mean amplitudes of provided data at chosen intervals to eradicate noise. The median filter measures the dip and azimuth of the targeted window and substitutes the dominant sample position with the median position of the amplitude. In addition, it also improves the spatial features, which are continuous, and reduces the noise of the seismic volume. Together, the duo of the mean and median filters reduces the noise while improving the signal-to-noise ratio that may deplete the fault and fracture features that can be solved using a diffusion filter [80]. The diffusion filter enhances the features that are continuous by keeping discontinuous features such as faults and fractures. If discontinuous features are detected, then smoothing does not take place, and filtering only applies to incoherent noise and insignificant stratigraphic features.  
Initially, a geostatistical filter was applied here, and a steering cube was generated using the fast Fourier transformation (FFT) of the background steering. The steering cube was prepared by using the dip and azimuth values [48]. The background steering cube was comprised of the structural dip and contained multiple filters to study the dip-associated faults, fractures, and sedimentary structures. Hence, FFT was applied to delineate the stratigraphic and structural information in the seismic volume. The dip was computed within a cube of \(3\times 3\times 3\) samples around each sample of the seismic window.  
The generated background steering cube was further incorporated while applying structural filtering. The dip-steered median filter (DSMF) was applied to remove random noise and to improve the dip and azimuth information of the generated steering cube. The noise removed was analyzed by the DSMF using the mathematics attribute. A diffusion filter was then applied on the dip-steered median filter to make the smoothing influence obsolete on the discontinuous features. The dip-steered diffusion filter (DSDF) enhanced the low-quality seismic data nearby faults and fractures. The two DSMF and DSDF filters were joined together to design an FEF. The FEF was based on a similarity threshold, and the data were smoothed (DSMF) away from the discontinuities and sharpened (DSDF) at the fault locations. Both similarity and steering cubes were incorporated in the study to establish a filter that had the same-sized window as the median filter. The results of the fault enhancement filter enhanced the sharpness of the faults and fractures, reduced the noise, and increased the smoothing of the reflectors. The FEF was then applied to the seismic volume to delineate the discontinuous features. The results provided a comparison of the original seismic (Figure 3a), dip-steered median filter (Figure 3b), dip-steered diffusion filter (Figure 3c), and the fault enhancement filter (Figure 3d) at inline 665. The results showed that the FEF effectively showcased the discontinuities marked by black arrows, which were not easily identified on the original seismic, DSMF, and DSDF data. The FEF and detailed steering cube were further used in the multi-attribute analysis to characterize the SSF and fracture network.