Seismic first break picking based on multi-task learning

Introduction Seismic first break (FB) picking helps us with near surface tomography, microseismic detection among other tasks. Using image semantic segmentation (ISS) networks to do so has been a hot topic in recent years, and multi-task learning has also demonstrated excellent data representation capabilities in several areas. Methods To improve accuracy, we combine the FB picking task with the seismic data reconstruction task, and propose an enhanced FB picking training method based on multi-task learning network. Specifically, we use two decoding branches of the same size in the network, which are the ISS decoding branch for the FB picking task, and the seismic feature learning decoding branch for the reconstruction task. The introduction of the seismic feature learning decoding branch will further help the network encoder to extract seismic effective features more efficiently, which will improve the accuracy of the ISS decoding branch, and ultimately improve the accuracy of the FB picking. During the training process, we use different loss functions for different decoding branches, and guide the network fitting through joint loss. In addition, we randomly add noise as well as random elimination to the seismic data to simulate the low SNR trace sets and bad traces that may exist in seismic data acquisition, and discuss the impact of different cases on the training results. Results and discussion The experimental results show that this method achieves more accurate FB picking results than the existing single-branch ISS methods, with an average picking error as low as 3.08 ms in the field data, and the percentage of traces with a picking error higher than 15 samples is as low as 0.03%, which is far superior to the network methods such as UNet, STUNet, SegNet, and Res-Unet, and effectively realizes the overall high-quality FB picking.