Most existing seismic data frequency enhancement methods have limitations. Given the advantages and disadvantages of these methods, this study attempts to apply deep learning technology to improve seismic data resolution. First, on the basis of the UNet deep learning method, which combines well-logging and seismic data, a synthetic seismic record is established with logging acoustic data and density, the borehole synthetic seismic record is labeled, and the borehole seismic trace data are taken as the input data. The training model of the borehole seismic trace data and the borehole synthetic seismic record is established to improve the medium- and high-frequency information in the seismic data. Second, the gate recurrent unit (GRU) is used to retain the low-frequency trend in the original seismic record, and the UNet and GRU results are combined to improve the medium- and high-frequency information while preserving the low-frequency information in the seismic data. Then, model training is performed, the model is applied to the three-dimensional seismic data volume for calculation, and the seismic data resolution is improved. The information extracted using our method is more abundant than that extracted using previous methods. The application of a theoretical model and actual field data shows that our method is effective in improving the resolution of poststack seismic data.

大多数现有的地震数据频率增强方法都有局限性。鉴于这些方法的优缺点，本研究尝试应用深度学习技术来提高地震数据分辨率。首先，基于UNet深度学习方法，结合测井和地震数据，利用测井声波数据和密度建立合成地震记录，对井眼合成地震记录进行标记，并获取井眼地震道数据作为输入数据。建立井下地震道数据和井下合成地震记录的训练模型，提高地震数据中的中高频信息。其次，利用门循环单元（GRU）保留原始地震记录中的低频趋势，将UNet和GRU结果结合起来，在保留原始地震记录中低频信息的同时，改善中高频信息。地震数据。然后进行模型训练，将模型应用于三维地震数据体进行计算，提高地震数据分辨率。使用我们的方法提取的信息比使用以前的方法提取的信息更丰富。理论模型和实际现场数据的应用表明，我们的方法可以有效提高叠后地震数据的分辨率。