## 1 Introduction  
The interpretation of seismic faults is an essential stage for both the exploration and development of reservoir characterization [1]. Because manual fault interpretation is time-consuming, implementing computer-aided techniques is tested in this study. Several approaches to extract faults from seismic data have been proposed over the past decades [2, 3]. Seismic discontinuity attributes were developed to visually detect faults and fractures [4]. To understand stratigraphic and structural geological features in seismic data volumes, seismic edge-detection algorithms have been very popular and extensively applied in the industry [5, 6, 7, 8]. Many researchers have utilized different attributes for detecting faults such as the curvature [2, 9], variance [10, 11, 12], semblance [4, 13], coherency [14, 15, 16, 17], eigenstructure [18, 19], fault likelihood [19, 20], similarity [8, 21, 22], entropy [23], flexure [3, 24, 25], gradient magnitude [26], chaos [26, 27], and derivatives [3, 18, 28, 29]. In earlier research, [PERSON] et al. (1991) presented how the azimuth and dip magnitude may reveal very tiny faults with movement substantially lower than that of a seismic wavelet [30]. Reflective curvature analysis has also been used to discover faults in migrating geological data using reflector geometry, as well as to map and predict fracture orientations and distribution via flexure and edge detection [24, 31]. However, misclassification is sometimes observed when attribute-based techniques fail CNN pre-trained on synthetic data [2; 34; 35]. However, the accuracy of these workflows when compared to multi-attribute-based classification is mostly reliant on the expertise of the interpreter in selecting a set of attributes to distinguish complex geologic features and faults. Furthermore, the attribute selection technique must be repeated from one seismic dataset to the next [36]. Many techniques and procedures for better fault detection have been proposed because of the severe limitations of the conventional fault interpretation, and some have been implemented successfully. These approaches include those that solely focus on enhancing the 3D representation of seismic data using attributes, meta-attributes, filters, soft computing, and machine-learning algorithms. Machine-learning techniques are often used in fields where conventional approaches are otherwise time-consuming due to the allegedly achieved results in these fields [4]. Filtering the data to eliminate the negative effects of noise may aid in smoothing reflectors and sharping for fault interpretation. Several noise attenuation techniques have proved useful in this area, whether utilized during the acquisition stage or subsequently [1]. Over the years, there have been tremendous advancements in the exploration of seismic faults in the context of reservoir characterization, but there is still an obvious research deficit. Prior research included attribute-based methods and even machine-learning strategies in an effort to improve fault detection methods. These attempts, which frequently rely primarily on manual attribute selection, have been hampered by the difficulty of consistently differentiating complex geological structures and faults across several seismic datasets [32; 36]. The critical importance of our study region, the Poseidon field in the Browse Basin, Australia, is highlighted by this knowledge gap. The distinctive geological features of the Poseidon field, characterized by complex normal faulting throughout the target Plover Formations, present a unique mix of difficulties and opportunities for seismic fault investigation. In this paper, we compared the traditional manual fault interpretation with a pre-trained CNN model on 3D seismic data from the Poseidon field of the Browse Basin, Australia. The motivation of this work is to demonstrate that the pre-trained CNN-based fault prediction is more efficient and time-saving when compared to variance-guided manually interpreted faults.