传统的地质方法对于地质剖面的确定过于依赖地震反射分辨率以及井数据的质量，针对实际情况中开发早期可用于标定的井数据数量极少，且传统地质建模方法生成地质剖面效率较低，难以支撑模型建立和频繁更新的问题，提出了基于改进Pix2Pix网络的地质剖面生成方法。首先对初始的三维数据进行切片处理，在深度学习网络综合分析基础上，构建了基于残差和多尺度判别器的Pix2Pix网络模型，在生成器部分引入残差机制以提高网络对地质特征的学习能力，并为模型设定多尺度判别器，增强网络的判别性能。使用油田的真实地震反射数据和地质剖面数据对模型进行训练，实验结果显示：网络模型在引进残差机制和多尺度判别器后性能得到显著提升，生成结果与真实地质剖面的SSIM （structural similarity）分数能够达到91.89%，生成结果中的地质特征与实际情况高度拟合。

The traditional geological method relies too much on the resolution of seismic reflection and the quality of well data for the determination of geological profiles. In view of the fact that the number of well data that can be used for calibration in the early stage of development is very small, and the traditional geological modeling method generates geological profiles. The efficiency is low and it is difficult to support model establishment and frequent updating. A geological profile generation method based on improved Pix2Pix network was proposed. Firstly, the initial three-dimensional data was sliced. Based on the comprehensive analysis of deep learning network, a Pix2Pix network model based on residual and multi-scale discriminator was constructed. The residual mechanism was introduced in the generator part to improve the learning ability of the network to geological features, and a multi-scale discriminator was set for the model to enhance the discriminant performance of the network. The real seismic reflection data and geological profile data of the oilfield were used to train the model. The experimental results show that the performance of the network model is significantly improved after the introduction of residual mechanism and multi-scale discriminator. The SSIM (structural similarity) score of the generated results and the real geological profile can reach 91.89%, and the geological features in the generated results are highly fitted with the actual situation.