Research on First-arrival Picking of Seismic P-wave Based on UNet++

Abstract

This paper studies the P-wave first-arrival picking based on UNet++. First, UNet++ is dimensionally reduced, and the original network is improved from the depth of the network structure and the operation of a single block. Then, the model selects a loss function and an optimizer to let the model find optimization goals and directions; then data preprocessing is performed, and the data with a signal-to-noise ratio less than 20db are screened out, and wavelet threshold denoising and normalization are performed on them; finally, training and validation are performed, and the performance on the validation set is selected. The optimal model is used as the final model. After the test of 150 test set data, it is proved that the proposed method is superior to STA/LTA and AR-AIC in the three indicators of mean, variance, and hit rate. The precision of the P-wave first arrival picked up by the method is as high as 98.00%. This work provides a new idea for automatic pickup of P-wave first arrivals.

Keywords

• earthquake
• deep learning
• unet++
• p-wave first arrival pickup