Abstract：The traditional first arrival picking method cannot take into account picking effect and efficiency,the algorithm stability is poor,and the industrial application has not been very mature.The first arrival picking method based on deep learning is time-consuming and labor-intensive,the process of data preprocessing is cumbersome,and the network structure is too complex,resulting in low training and test efficiency.Combining the advantages of U-Net with those of SegNet,a new hybrid network U-SegNet is constructed,and based on which first arrivals can be picked automatically.Based on the SegNet structure,U-SegNet provides multi-scale information of the encoder network by fusing jump connections information before the deconvolution layer of the decoder network to obtain better performance,and its upsampling operation changes the deconvolution in U-Net to unpooling.Because the pooling index is passed to the upsampling layer,the network model converges faster.Therefore,the U-SegNet network structure is more conducive to segmenting the background noise area and the area where background noise and valid signal overlap,thereby improving the accuracy of first arrival picking.The first arrival automatic picking process based on U-SegNet includes making a training data set,designing a network model,training the network model,testing the network model and applying it to real seismic data.Tests and applications of the U-SegNet model show that the picking efficiency of the proposed method is about 2.2 times that of a commercial software.It is easy to be industrialized and has a good future in large-scale application.
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