改进的整体嵌套边缘检测地震断层识别技术

断层解释的精度和效率对油气藏的勘探与开发非常重要。传统的断层解释方法多以人工为主，其依赖解释人员的经验且耗时较长；常规自动断层解释方法主要是分析地震数据的不连续性，往往涉及多个参数，因而断层解释精度多依赖选取的参数。近年来，随着深度学习技术的发展，非线性卷积神经网络能够描述地震数据中的不连续特征。为此，引入深度学习中的边缘检测技术，即整体嵌套边缘检测（Holistically-Nested Edge Detection，HED）网络，并根据地震数据和断层特点对网络结构进行改进和优化，提出适用于地震断层智能解释的改进HED （Improved HED，IHED）网络。主要步骤包括：①将原始二维HED网络推广至三维，搭建三维HED网络； ②根据HED网络的多尺度特点，调整三维HED网络构架； ③利用三维合成地震数据及其标签数据训练得到三维IHED模型，将该模型用于实际地震数据进行断层智能解释。与相干体算法和U-Net模型相比，三维IHED模型对断层预测的准确性更高，连续性更好。该方法为地震断层智能识别提供了一条可靠途径。

Seismic fault interpretation based on improved holistically-nested edge detection

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.