3D Seismic Data Reconstruction Based on UNet in Different Domains
Due to the limitations of natural environments and the constraints of economic con-dition,acquired seismic data often contains missing traces,which seriously affects subse-quent seismic data processing and interpretation.At present,most deep learning methods conduct the interpolation of seismic data within a specified domain.To compare the differ-ence of seismic data reconstruction results in different domains,based on UNet network,this paper compares five seismic data interpolation methods in different domains:time-space domain,time slice,frequency-space domain,frequency-domain slicing by shot and 3D meth-od.We conducted a series of experiments on synthetic and field data.The 3D reconstruction results,2D slice,SNR in seismic data reconstruction with different missing ratios and time consumption comparison of five methods comprehensively show the differences among vari-ous methods in seismic data missing problem.Experimental results indicate that all five methods are able to reconstruct missing data to some extent.3D method can achieve the highest reconstruction accuracy,and compared with methods based on the f-x domain,the reconstruction signal-to-noise ratio is improved by nearly 2 dB.However,because of using 3D data blocks as input to train the network,the 3D method is the most time-consuming,and one round of network training takes 70～170 minutes more than other methods.In addi-tion,the reconstruction results of seismic data with different missing rates show that 3D methods and f-x domain based methods have more advantages in reconstructing high missing rate seismic data.
seismic data reconstructiontime-space domaintime slice,frequency-space do-mainfrequency-domain slicing by shot3D methodUNet
万方数据
维普
