Firstarrival traveltime pickup method for seismic data based on attention U-Net
The first arrival pickup is the basis for near-surface first arrivals inversion and statics correction,an indispensable part of subsequent seismic data processing.With the ever increasing seismic data volumes,more complex near-surface conditions,and low Signal-to-Noise Ratio(SNR),traditional automatic methods can not meet the pickup requirements.So a more accurate automatic first arrival pickup method is crucial.The main contributions of this paper are as follows:This study proposed an attention-U-Net and applied it to the first arrival pickup of seismic data with a low signal-to-noise ratio and strong near-surface influence.Firstly,the attention gates were integrated into a standard U-Net model,which progressively suppressed features in irrelevant background parts and improved the picking accuracy.Secondly,this paper proposed to use seismic waveform data and energy semblance data as two channels data body,which makes the U-Net focus not only on the phase but also on the energy attribute of the first arrival traveltime.Thirdly,we used a single channel"0-1"label as the U-Net's output,which can alleviate the unbalanced distribution of label value and improve the prediction accuracy.Then,we proposed a scheme to construct a synthetic dataset containing characteristics of seismic field data to test the robustness of the U-Net and proved that the Dice-loss function could improve the pickup accuracy.At last,for mountain field,seismic data,the U-Net proposed in this paper has better adaptability to strong noise interference,missing and bad traces,and complex near-surface conditions,significantly reducing labor expense.
First arrival pickupAttentionComplex near-surface conditions
万方数据
维普
