为了识别煤系金属矿床成矿过程中的关键地质构造，以鄂尔多斯盆地临兴区块为研究对象，依托前人研究结果、区块地质资料、地震以及测井等地球物理数据，构建了基于U-Net网络的地质构造识别模型，用于预测构造火山活动等事件，以分析矿床形成的关键地质因素。

首先，基于叠后地震资料，利用平面波破坏滤波器技术实现绕射波分离与成像，提取小尺度关键地质信息；其次，利用知识图谱结合研究区实际情况开展属性优选，获得方差、瞬时频率和均方根振幅等反映构造特征的优势属性。以研究区三维地震属性体作为训练样本，基于知识图谱建立映射标签，引入卷积块注意力机制提出了U-Net多属性融合模型。该模型以三通道地震属性作为输入，地下关键地质构造(如断层或火山通道)作为输出，进而构建关键地质构造智能化识别方法。

研究结果表明：基于知识图谱建立的U-Net网络，在煤系金属矿床关键地质构造识别上具有较高的效率和准确性，可直观刻画关键地质构造的三维展布特征，降低断层解释人为不确定性，为煤系金属矿床研究提供有效的技术支撑。

In order to identify the key geological structures in the metallogenic process of coal measures metal deposits, the Linxing block of Ordos Basin was taken as the research object. Based on the previous research results, geological data of the block, seismic and logging data, a geological model including tectonic volcanic activities and other events was constructed to analyze the key geological factors of deposit formation.

Firstly, based on the post-stack seismic data, the diffraction wave separation and imaging are realized by using the plane wave destruction filter technology, and the small-scale key geological information is extracted. Secondly, the attribute optimization is carried out by using the knowledge map combined with the actual situation of the study area, and the dominant attributes reflecting the structural characteristics such as variance, instantaneous frequency and root mean square amplitude are obtained. Taking the three-dimensional seismic attribute volume of the study area as the training sample, the mapping label is established based on the knowledge graph, and the U-Net multi-attribute fusion model is proposed by introducing the convolution block attention mechanism. The model uses three-channel seismic attributes as input and underground key geological structures ( such as faults or volcanic channels ) as output, and then constructs an intelligent identification method for key geological structures.

The results show that the U-Net based on the knowledge map has high efficiency and accuracy in the identification of key geological structures of coal-bearing metal deposits. It can intuitively depict the three-dimensional distribution characteristics of key geological structures, reduce the artificial uncertainty of fault interpretation, and provide effective technical support for the study of coal-bearing metal deposits.