# Seismic Identification and Characterization of Deep Strike-Slip Faults in the Tarim Craton Basin
## 6 Discussion
### Seismic Attribute Fusion Technology  
Seismic attribute fusion can synthesize the advantages of multiple attributes, and it is a favourable tool for identifying strike-slip faults in cratons with complex tectonic evolutions. Based on the analysis and comparison of twelve seismic attributes, two attributes that are most suitable for the identification of deep strike-slip faults in a craton were selected in this paper: the amplitude gradient attribute and the ant tracking attribute. The ant tracking method is a fully developed fault identification attribute in industry. Ant tracking was used to analyze meso-scale fractures as natural fracture corridors; however, for branch faults with complex and small slip spacings, ant tracking predicted a lot of false fault information. The amplitude gradient attribute measures the rate of change in amplitudes along a seismic trace and has also been used for fault identification [10]. The amplitude gradient attribute was used to identify faults to predict the position of reservoirs and obtained results that are clearer and more accurate in the section; however, the detailed characterization is insufficient in the plane section [12]. The main reasons are as follows: (1) in the ant tracking results, faults of different scales are often mixed with sequence and sedimentary characteristics, especially when seismic dataset noise or geological sedimentary background is complex, and it is difficult to distinguish the fake from the real; (2) the interpretationof the ant tracking data is poor in vertical sections of deep strata with complex structures; and (3) the amplitude gradient attribute is based on searching the most messy direction of the amplitude gradient to find the reliable location of the fault, which also leads to a low resolution of the fault inside [60]. Image fusion technology, through image processing and computer techniques, enables the extraction of optimal information from each channel, enhancing the utilization rate of image data and improving interpretation accuracy and reliability [61]. Drawing from the success of image fusion in various fields, mesh subdivision, threshold analysis, ellipsoid expansion, wavelet transform, and other algorithms are implemented in this study to fuse the ant tracking and amplitude gradient attributes, resulting in high-precision and high-resolution fault images. The fused attributes produce a single, detailed image that highlights potential fault locations within the seismic dataset. Utilizing image fusion technology, the subsurface geology can be depicted with greater detail and accuracy compared to individual seismic attributes alone. The reliability of prediction outcomes is confirmed through comparisons with actual drilling and production data in various aspects [62]. But the primary disadvantages of this research include not incorporating deep learning, limitations in feature extraction, dependence on expert domain knowledge, difficulty in handling noisy data, scalability issues with large datasets, limited adaptability to varying geological conditions, potential interference from other geological features, longer processing times, and challenges in capturing non-linear relationships within the data. Furthermore, the fault prediction results obtained from this method offer significant guidance for fault mode studies and oil and gas production endeavours. The results of this study have significant implications for geological exploration and oil and gas production, particularly in improving the accuracy of fault identification within complex cratonic basins. By enhancing the prediction of reservoirs, especially in deep carbonate strata, our methods provide critical guidance for more efficient resource extraction. However, the approach also has limitations, including challenges in handling noisy data, adapting to diverse geological conditions, and capturing non-linear features. Additionally, the reliance on expert domain knowledge may limit its generalizability.