[1]高新成,杜功鑫,王莉利,等.深度学习在地震初至拾取中的应用综述[J].计算机技术与发展,2022,32(08):1-6.[doi:10. 3969 / j. issn. 1673-629X. 2022. 08. 001]
　GAO Xin-cheng,DU Gong-xin,WANG Li-li,et al.Application of Deep Learning in Earthquake First Break Picking[J].,2022,32(08):1-6.[doi:10. 3969 / j. issn. 1673-629X. 2022. 08. 001]
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深度学习在地震初至拾取中的应用综述()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
32
期数:
2022年08期
页码:
1-6
栏目:
综述
出版日期:
2022-08-10

文章信息/Info

Title:
Application of Deep Learning in Earthquake First Break Picking
文章编号:
1673-629X(2022)08-0001-06
作者:
高新成1 ; 杜功鑫2 ; 王莉利2 ; 李 强2 ; 柯 璇3
1. 东北石油大学 现代教育技术中心,黑龙江 大庆 163318;
2. 东北石油大学 计算机与信息技术学院,黑龙江 大庆 163318;
3. 东北石油大学 地球科学学院,黑龙江 大庆 163318
Author(s):
GAO Xin-cheng1 ; DU Gong-xin2 ; WANG Li-li2 ; LI Qiang2 ; KE Xuan3
1. Modern Education Technology Center,Northeast Petroleum University,Daqing 163318,China;
2. School of Computer & Information Technology,Northeast Petroleum University,Daqing 163318, China;
3. School of Earth Science,Northeast Petroleum University,Daqing 163318,China
关键词:
人工智能; 深度学习; 地震勘探; 初至拾取; 神经网络
Keywords:
artificial intelligence; deep learning; seismic exploration; first arrival picking; neural network
分类号:
TP183;P163. 4
DOI:
10. 3969 / j. issn. 1673-629X. 2022. 08. 001
摘要:
随着计算机硬件技术的不断提高,人工智能技术在各个行业的广泛应用,地震学界开始探索用深度学习算法处理地震初至波数据,为初至波的拾取研究提供了崭新的方向。 为了解决传统地震波初至拾取方法对低信噪比资料拾取精度较低、算法鲁棒性较差等缺点,提高地震初至拾取速度和效率,减少耗费的人力以及人为拾取所产生的误差,该文系统地介绍了地震事件初至波拾取的常用传统方法,详细地阐述了深度学习领域内各种经典神经网络模型在地震初至波拾取中应用现状。 通过实例对比分析卷积神经网络、深度信念网络、生成对抗神经网络和深度递归神经网络等模型在地震初至波拾取中的应用效果,讨论总结了深度学习在地震初至波拾取领域内应用存在问题和应用前景,为今后地震初至拾取研究提供新的思路。
Abstract:
With the continuous improvement of computer hardware technology and the wide application of artificial intelligencetechnology in various industries,the seismological community began to explore the application of depth learning algorithm to processseismic first break data,which provides a new direction for the research of first break picking. In order to solve the shortcomings oftraditional seismic first break picking methods,such as low picking accuracy of low signal-to-noise ratio data and poor robustness of thealgorithm,improve the speed and efficiency of seismic first break picking,and reduce the labor consumption and human error,we systematically introduce the common traditional methods of seismic event first break pickup and expound in detail the application status ofvarious classical neural network models in the field of depth learning in seismic first break pickup. By examples,we compare and analyzethe application effects of convolution neural network,depth belief network,generating antagonistic neural network and depth recursiveneural network in seismic first break pickup. The existing problems and application prospects of depth learning in the field of seismic firstbreak pickup are discussed and summarized,which provides a new idea for the research of seismic first break pickup in the future.

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更新日期/Last Update: 2022-08-10