Deep Learning for 3D Seismic Fault Prediction Using Convolutional Neural Networks (CNNs)

The comprehension of seismic faults is essential for generating prospects, modeling reservoirs, and assessing CO ₂ storage. Identifying faults in complex tectonic regimes presents significant challenges, especially in areas that have undergone multiple phases of tectonic activity. Even with progress in structural seismic attributes and machine learning, interpreters frequently depend on manual techniques to examine complex fault systems. This work introduces a method for predicting 3D seismic faults through the application of Convolutional Neural Networks (CNNs), which effectively overcomes the constraints associated with conventional interpretation techniques. The project utilizes Convolutional Neural Networks (CNNs) to illustrate the effective use of seismic attributes in training models that can identify faults with high accuracy and consistency. This method, in contrast to manual interpretation, minimizes time consumption and subjective error by utilizing automated learning techniques, thereby enhancing reproducibility, efficiency, and reducing interpreter bias. The research emphasizes the increasing significance of strong computational tools in geophysical engineering, particularly as seismic datasets grow more complex and extensive. Additionally, the framework plays a significant role in strengthening confidence in AI-assisted geological analysis through the validation of its performance using real-world data. This approach minimizes dependency on manual processes while showcasing the capability of machine learning to enhance reliable, scalable, and objective workflows for subsurface interpretation.