基于U-Net网络的FWI地震低频恢复方法

FWI seismic low frequency recovery method based on the U-Net

由于实际地震资料中缺乏低频数据,使得全波形反演易陷入局部极小值,导致反演质量差,结果不可靠。为解决这一问题,本文利用数据驱动低频恢复映射思想,分别利用高通和低通滤波器从原始数据中分离出高频数据和低频数据,对其进行一系列数据预处理操作,将处理后的数据作为模型的训练集;利用U-Net网络为基础构建模型,建立高低频之间的映射关系。为了有效防止模型过拟合,本文在U-Net模型基础上添加了Dropout层和批处理层。利用训练后的模型从高频数据中预测对应低频数据并进行逆数据预处理,对比分析逆数据预处理后的预测低频数据和真实低频数据之间误差,并利用多尺度全波形反演在洼陷模型和Marmousi模型进行有效性验证。实验结果表明:训练与测试数据的预测低频与真实低频数据的平均相对误差为5.02%和13.32%,误差较小,数据吻合良好;洼陷模型、Marmousi模型以及实际数据的反演结果表明加入预测低频后反演质量得到显著提高,并且对处理含较大噪声的数据也有很好的效果。

The lack of low-frequency data in actual seismic data makes the full waveform inversion(FWI)tend to fall into the local minimum,resulting in poor inversion quality and unreliable results.In view of this,data-driven low-frequency recovery mapping was adopted in this study.First,high-pass and low-pass filters were employed to separate high-frequency and low-frequency data from raw data,respectively,and then data preprocessing was carried out.The processed data were used as the training set of the model.Then,the model was built based on the U-Net to establish the mapping relationship between high and low frequencies.To effectively prevent the model from overfitting,the dropout layer and batch processing layer were added based on the U-Net model.Finally,the trained model was used to predict the corresponding low-frequency data from the high-frequency data and conduct inverse data preprocessing.The errors between the predicted low-frequency data after inverse data preprocessing and the real low-frequency data were compared and analyzed,The effectiveness of multi-scale FWI was verified using the depression and Marmousi models.The experimental results show that the average relative errors between the predicted low-frequency data and the real low-frequency data were 5.02%and 13.32%,respectively for training and test data,indicating small errors and high data coincidence.The inversion results of the depression model,the Marmousi model,and actual data show that the prediction of low-frequency data significantly improved the inversion quality and delivered a great performance in the processing of data with much noise.