CUNet deep learning method and its application in fault recognition
Intelligent fault recognition plays a very important role in the fine interpretation of seismic data.However,at present,the intelligent fault recognition method still has problems such as difficulty in model training and damaged signal details in feature image extraction.In view of these problems,this paper proposes an improved CUNet(Convolution Unity Networking)fault intelligent recognition method based on the UNet network structure and the idea of VNet deep learning network.The CUNet fault identification method employs convolution operations to replace the max-pooling in downsampling and 3D transpose convolution operations to replace the convolution in upsampling,thus enhancing the receptive field of the CUNet structure and alleviating the signal detail loss during upsampling and downsampling operations.The experimental results show that the accuracy of CUNet network structure reaches 94.3％,which is 1.4％higher than that of UNet network structure,and has a better anti-over-fitting effect.According to the seismic geological characteristics of faults,the CUNet network structure is applied to the intelligent fault identification of seismic images.The application results show that the network structure not only detects the fault features more accurately,but also depicts the fault distribution more carefully,and greatly saves the calculation time.
Fault intelligent identificationDeep learningCUNet structureAnti-overfittingCharacteristics of fault
NETL
NSTL
万方数据
