000144623 001__ 144623
000144623 005__ 20250923084421.0
000144623 0247_ $$2doi$$a10.1111/1365-2478.13592
000144623 0248_ $$2sideral$$a139442
000144623 037__ $$aART-2024-139442
000144623 041__ $$aeng
000144623 100__ $$aWu, Yinghe
000144623 245__ $$aAutomatic seismic first-break picking based on multi-view feature fusion network
000144623 260__ $$c2024
000144623 5203_ $$aAutomatic 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.
000144623 540__ $$9info:eu-repo/semantics/closedAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000144623 590__ $$a1.8$$b2024
000144623 592__ $$a0.631$$b2024
000144623 591__ $$aGEOCHEMISTRY & GEOPHYSICS$$b54 / 100 = 0.54$$c2024$$dQ3$$eT2
000144623 593__ $$aGeophysics$$c2024$$dQ2
000144623 593__ $$aGeochemistry and Petrology$$c2024$$dQ2
000144623 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000144623 700__ $$aPan, Shulin
000144623 700__ $$aLan, Haiqiang
000144623 700__ $$0(orcid)0000-0002-3424-7744$$aBadal, José$$uUniversidad de Zaragoza
000144623 700__ $$aWei, Ze
000144623 700__ $$aChen, Yaojie
000144623 7102_ $$12004$$2405$$aUniversidad de Zaragoza$$bDpto. Física Teórica$$cÁrea Física Teórica
000144623 773__ $$g72, 9 (2024), 3547-3559$$pGeophys. prospect.$$tGEOPHYSICAL PROSPECTING$$x0016-8025
000144623 8564_ $$s19010785$$uhttps://zaguan.unizar.es/record/144623/files/texto_completo.pdf$$yVersión publicada
000144623 8564_ $$s2401300$$uhttps://zaguan.unizar.es/record/144623/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000144623 909CO $$ooai:zaguan.unizar.es:144623$$particulos$$pdriver
000144623 951__ $$a2025-09-22-14:35:58
000144623 980__ $$aARTICLE