Seismic facies identification technology is a powerful tool for sedimentary environment analysis and reservoir prediction.The traditional artificial Seismic facies identification method not only has a large workload but also has very low efficiency.At present,using the deep learning method can greatly improve the efficiency of Seismic facies identification,but limited by the limited data sets and network feature extraction ability,the recognition effect of seismic facies with a small number of samples are poor.To solve the above problems,a multi-attribute Seismic facies identification method based on an improved U-Net is proposed in this paper.Firstly,the elastic distortion algorithm is used to augment the data set,and the multi-attribute data volume after attribute selection is used as the input data to improve the quantity and quality of the input data.Secondly,the attention mechanism is introduced to add weight parameters to the features extracted by the network to improve the feature extraction ability of the U-Net network.And the Dice index is introduced into the loss function to solve the problem of sample imbalance.Numerical experiments show that the accuracy of seismic phase prediction can be effectively improved based on the improved U-Net model.
维普
万方数据