Automatic seismic first‐break picking based on multi‐view feature fusion network

• Published in: Geophysical Prospecting 2024-11, Vol.72 (9), p.3547-3559
• Main Authors: Wu, Yinghe, Pan, Shulin, Lan, Haiqiang, Badal, José, Wei, Ze, Chen, Yaojie
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Artificial intelligence; automatic first‐break picking; Data analysis; Data processing; Dimensional analysis; Earthquake resistance; Feature extraction; feature fusion; LSTM network; multi‐network learning; Networks; Order picking; Re‐AG‐Unet network; Seismic data; Seismic data processing
• ISSN: 0016-8025; 1365-2478
• DOI: 10.1111/1365-2478.13592

Automatic first‐break picking is a basic step in seismic data processing, so much so that the quality of the picking largely determines the effect of subsequent processing. To a certain extent, artificial intelligence technology has solved the shortcomings of traditional first‐break picking algorithms, such as poor applicability and low efficiency. However, some problems still remain for seismic data, with a low signal‐to‐noise ratio and large first‐break change leading to inaccurate picking and poor generalization of the network. In order to improve the accuracy of the automatic first‐break picking results of the above seismic data, we propose a multi‐view automatic first‐break picking method driven by multi‐network. First, we analysed the single‐trace boundary characteristics and the two‐dimensional boundary characteristics of the first break. Based on these two characteristics of the first break, we used the Long Short‐Term Memory and the ResNet attention gate UNet (resudual attention gate UNet) networks to extract the characteristics of the first arrival and its location from the seismic data, respectively. Then, we introduced the idea of multi‐network learning in the first‐break picking work and designed a feature fusion network. Finally, the multi‐view first‐break features extracted by the Long Short‐Term Memory and resudual attention gate UNet networks are fused, which effectively improves the picking accuracy. The results obtained after applying the method to field seismic data show that the accuracy of the first break detected by a feature fusion network is higher than that given by the above two networks alone and has good applicability and resistance to noise.