Fault Identification of U-Net Based on Enhanced Feature Fusion and Attention Mechanism

Abstract

Accurate fault identification is essential for geological interpretation and reservoir exploitation. However, the unclear and noisy composition of seismic data makes it difficult to identify the complete fault structure using conventional methods. Thus, we have developed an attentional U-shaped network (EAResU-net) based on enhanced feature fusion for automated end-to-end fault interpretation of 3D seismic data. EAResU-net uses an enhanced feature fusion mechanism to reduce the semantic gap between the encoder and decoder and improve the representation of fault features in combination with residual structures. In addition, EAResU-net introduces an attention mechanism, which effectively suppresses seismic data noise and improves model accuracy. The experimental results on synthetic and field data demonstrate that, compared with traditional deep learning methods for fault detection, our EAResU-net can achieve more accurate and continuous fault recognition results.