## 3 Results  
### Seismic Data Conditioning  
Seismic data acquisition and processing may introduce noise in the seismic data. Geometric attributes such as discontinuity or curvature are impacted due to acquisition footprint, coherent noise, and certain processing artifacts [26]. Seismic data conditioning is the key process of removing noise from seismic data and for improving the seismic reflector continuity and fault edges. The seismic conditioning process can improve the clarity of the signal that arises from changes in geology. The main objective of applying the seismic data conditioning process is to achieve a higher signal-to-noise ratio while preserving  
Figure 3: The multistep workflow is comprised of four main steps: 1. Data conditioning; 2. Edge detection and enhancements; 3. Automatic fault extraction; and 4. Fault data analysis.  
subtle details like faults, even small-scale faults, and amplitude variance due to geological features. Applying a particular attribute with default parameters to unconditioned input data often leads to sub-optimal or even undesired results. The objective is to find a seismic filter that tones down the unrelated discontinuities while leaving the actual ones unaltered. Noise in seismic data cannot be removed in one step necessarily. Major improvements in discontinuity attributes can be achieved in three steps:  
Conditioning and optimizing the input seismic and dip steering data.  
Testing and choosing the best parameters for attribute generation.  
Applying the appropriate filter(s) enhancements on the resultant discontinuity attribute.  
There are several post-stack seismic data smoothing filters available to reduce unwanted signals from seismic data and to improve seismic reflector continuity by revealing structural and stratigraphic details. Dip Steer Median Filter (DSMF) is structural oriented filter used in this study that preserves the edges while applying smoothing. Other seismic filters include bandpass filtering, Mean, Median, and Gaussian smoothing, etc. Other worthwhile methods in this regard are based on a Radon transform type filter [27], on singular value decomposition of the seismic data [28; 29], or adaptative subtraction algorithms [30].  
The standard median filter can subdue the random noise in seismic data but degrades the edges of laterally continuous events such as faults. Therefore, our choice was an edge-preserving dip-steered structure-oriented median filtering to suppress the noise and to improve the signal-to-noise ratio. The dip steer median filter process creates dip and azimuth steering volumes that are needed to orientate the smoothing filters along with the reflectors. The parametrization of DSMF is a very important aspect which needs to be taken care of. The state-of-the-art visualization tools at the interpreter's disposal enable him to test and select the best parameters interactively to condition the seismic data. To create the resultant steering cube using DSMF, the filter size should be decided based on geological complexity and signal-to-noise ratio. This parameter needs to be large enough to take the traces across faults into account while maintaining the structural trends preserving the edges. The resultant volumes capture the dip and azimuth of the dominant reflectors representing real geological trends as shown in Figure 4. Seismic data conditioning retains seismic amplitude variations along with the reflectors by decluttering the seismic data. This improves the reflector's cohesion while enhancing the fault signature. It also helps to remove 'jitter' and acquisition footprints along with the reflectors.  
Figure 4: Seismic data conditioning and enhancements applied on input seismic data. (**a**) Original seismic data before the application of any enhancements. (**b**) Data enhanced after DSMF/Structure-Oriented Filtering. The Chimney effect in the vertical ellipse is visible. Red and yellow pointers compare the continuity in horizons and faults respectively.