Seismic data is a critical source for examining and understanding subsurface structures. It is used in various fields such as oil exploration, geological research, mining, underground infrastructure planning, environmental monitoring, and conservation. The analysis and interpretation of these data are often complex and time-consuming. Deep learning, on the other hand, is recognized for its ability to identify complex patterns in large datasets. Deep learning models can be used to address challenges such as understanding complexity, noise reduction, and feature extraction in seismic data. The role of deep learning methods in seismic data analysis is increasingly significant because these techniques offer the potential to obtain more accurate results and discover new opportunities in seismic interpretation. This contributes to making the seismic interpretation process more efficient and enhancing the understanding of subsurface structures. This study focuses on interpreting seismic data using deep learning methods and examines how deep learning models can be utilized in seismic interpretation. The results of the study demonstrate that deep learning models can effectively address complexity in seismic data and perform automatic feature extraction. These findings highlight the potential of deep learning methods in the field of seismic interpretation and shed light on future research directions.

Keywords - Artificial intelligence; deep learning; seismic data

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