# Enhancing Channelized Feature Interpretability Using Deep Learning Predictive Modeling
## 1 Introduction  
The essence of modern sedimentary basins highlights the elements of meandering channels and corresponding meandering belts. Identification of fluvial channels has been considered crucial and requires expert knowledge by looking into the factors controlling the sandstone body shape, dimensions, connectivity, and internal heterogeneity [1]. Conventional subsurface facies analysis for meandering fluvial channels was initially employed based on the characteristics of vertical profile (according to [PERSON]'s law of facies succession) and morphology of the channels [2]. Since channels pose as an important hydrocarbon indicator for the elastic depositional environment, whether in exploration or development projects, the conventional method of detecting sand-filled geobodies, guided by multiple seismic attribute extraction, is considered exhaustive yet time-consuming, contrasting with the growing demand for higher-resolution yet higher-bandwidth seismic data. Although seismic attributes have the capability to enhance details leading to better geological and geophysical data, they also have the tendency to create new interpretational pitfalls. The growth of seismic attribute technology in accelerating the advancement of exploration and development started in the late 1960s, with identification of bright spots through seismic reflection traits [3]. Most positive curvatures were capable of illuminating crispier lineaments of meandering channels compared to coherence attributes [4]. Sweetness was proven as an important attribute for predicting net-to-gross ratio using semi-quantitative application for different superimposed channels [5]. In between the captured timeline, pattern recognitionor neural network-based analysis on seismic attributes since the 1990s has gradually brought significant improvement in defining structures or depositional environments. Conventional technology has emphasized the usage of seismic reflection patterns to predict depositional environment, structural deformation, and diagenetic alteration by constraining lithology and porosity information. Knowledge of and experience with seismic patterns or textures stored in the brain were applied by human interpreters to infer the possible geological setting using digital or analog data, combining geology, geophysics, and petrophysical disciplines [6]. Understanding the geomorphology of channels, specifically in elastic depositional environments, showcased the conventional application of multiple seismic attributes to extract information such as segments of channels, discontinuities, dip, and azimuth. With the advancement of neural network technology over the past twenty years, extracting continuous channel bodies based on the information of related seismic facies in the known settings, for instance, helps interpreters to accelerate their structural interpretation, and hence, mapping for challenging structures becomes easier for further analysis in reservoir characterization, inversion, geohazard avoidance, and many more applications. We implement a deep learning (DL) predictive modeling scheme to improve channel interpretation by exploiting classified seismic attributes [7] as the input to infer quantitative property/structural information (Table 1) and 2D U-Net Network structure, due to its reliability and stable performance, with minimum time required for debugging. Different types of ground truth are attached to imitate the actual process used by human interpreters on synthetic and real field data. The term \"Ground Truth\" in deep learning has been defined as a conceptual term relative to the knowledge of truth concerning a specific question, with ideal expected results and statistical models or research hypotheses to be supported. By taking image segmentation challenges into seismic images perspectives, ground truth may specifically refer to pixel-based identification compared to the input data.