# Structural Augmentation in Seismic Data for Fault Prediction
## 1 Introduction  
As a typical component of the geological structure, faults can serve as potential channels for oil and gas migration and accumulation [1; 2; 3] or reveal the spatial distribution and heterogeneity of subsurface targets [4; 5]. Therefore, fault identification methods have become indispensable technologies in seismic data interpretation [6; 7]. However, the complex structures and low-quality information contained in seismic data become unavoidable challenges, especially for ultra-deep fault interpretation [8]. To accurately and effectively identify faults from seismic data, many researchers have proposed various practical strategies. In the early days, researchers used the human-computer interaction method to manually interpret the seismic reflection discontinuity caused by the fault [9; 10; 11]. However, using only seismic signals to manually identify faults often produces strong human-oriented recognition results, and the interpretation results are severely limited by the signal-to-noise ratio and seismic data resolution. Therefore, the joint well-seismic interpretation technologies are derived [9; 12; 13]. Through the combination of log and seismic data, the accuracy of fault interpretation is effectively improved. However, these fault identification technologies rely on manual fault labeling, which is highly time-consuming, especially for 3D seismic data. Therefore, some semi-automatic methods [14; 15] and automatic fault recognition methods [16; 17; 18] have been proposed to reduce the workload of manual interpretation. These methods suggest that the purpose of automatic fault identification can be achieved by calculating and highlighting the discontinuity of seismic events. Coherence attribute as a classicautomatic fault identification technology is widely used in seismic data interpretation, and many coherence-based algorithms have been developed, such as the first-generation coherence algorithm [16], the least-square coherence algorithm [17], the semblance-based coherence algorithm [18], the eigenstructure-based coherence algorithm [19], the frequency-decomposed coherence algorithm [20], the predictive coherence algorithm [21], and the structure-oriented coherence [22]. In addition to coherent attributes, curvature attributes are also commonly used in seismic data interpretation. Since [PERSON] [23] proposed the Gaussian Curvature Analysis (GCA) method to detect abnormal strain regions in the structures, many curvature-based algorithms have been discussed, such as surface curvature algorithm [24], volumetric curvature algorithm [25], curvature gradient [26], and flexure attribute [27, 28]. However, the fault identification methods based on the discontinuity of the reflection events are sensitive to seismic noise [29, 30]. Therefore, many researchers have developed fault identification methods based on anti-noise or fault structure enhancement [31, 32, 33, 34, 35, 36], such as ant tracking [37, 38], spectral coherence [20], multi-azimuth coherence [39], fault voting [40], and multi-sensitive attribute fusion [41, 42, 43]. However, these enhanced recognition methods with structural constraints may still produce erroneous recognition results, especially in the data area where faults are adjacent to each other [44]. To further improve the accuracy of fault prediction, artificial intelligence algorithms are introduced in this paper to identify faults. Artificial intelligence methods have surprising interdisciplinary applicability [45, 46, 47, 48, 49, 50] and have been introduced into the field of geosciences [51, 52], such as seismic imaging [53], seismic waveform classification [54], event localization [55, 56], earthquake prediction [57, 58], and earthquake warning [59]. Recently, some researchers began to use artificial intelligence to identify faults and obtained effective results. These methods can be mainly divided into three categories: The first category of methods trains the neural networks with field seismic data and manually interpreted labels [60, 61, 62, 63].