改进的整体嵌套边缘检测地震断层识别技术

The accuracy and efficiency of fault interpretation greatly affect the exploration and development of oil and gas reservoirs. The traditional manual fault interpretation method relies on the experience of interpreters and takes a long time; the conventional automatic fault interpretation method mainly interprets faults by discontinuity analysis of seismic data and often contains multiple parameters, and thus its accuracy in fault interpretation mostly depends on the selected parameters. With the development of deep learning in recent years, the convolutional neural networks (CNNs) with nonlinear properties can also describe the discontinuous characteristics of seismic data. Therefore, an edge detection technology in deep learning, i.e., the holistically-nested edge detection (HED) network, is introduced in this study, and the network is improved and optimized on the basis of the cha-racteristics of seismic data and seismic faults, which leads to the improved HED (IHED) network suitable for intelligent seismic fault interpretation. The main steps are as follows: ① The original two-dimensional (2D) HED network is extended to a three-dimensional (3D) version, and thus a 3D HED network is constructed; ② the architecture of the 3D HED network is adjusted considering the multi-scale property of the network; ③ the 3D HED network is trained with 3D synthetic seismic data and corresponding label data for a 3D IHED model, and then the 3D IHED model is applied to field data for seismic fault interpretation. Compared with the coherence cube algorithm and U-Net model, the 3D IHED model features higher accuracy in the prediction of faults and better continuity. The proposed model provides an efficient and reliable new idea for intelligent fault interpretation.