基于循环对抗神经网络的快速最小二乘逆时偏移成像方法

P631.4; 最小二乘逆时偏移成像方法因计算量巨大,限制了其大规模的工业应用.基于此,建立循环对抗神经网络表征Hessian矩阵的逆,构建逆时偏移结果和反射系数之间的映射关系.通过建立的神经网络对逆时偏移成像结果进行去模糊化处理,提高成像质量,同时大幅减少计算时间.将训练好的网络应用于Marmousi模型和Sigsbee2A模型的逆时偏移结果.结果表明,本文方法在不显著增加计算量的情况下较好地提高了逆时偏移成像质量....

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Published in中国石油大学学报（自然科学版） Vol. 47; no. 3; pp. 55 - 61
Main Authors 黄韵博, 黄建平, 李振春, 刘博文
Format Journal Article
LanguageChinese
Published 中国石油大学(华东)地球科学与技术学院,山东青岛 266580 01.06.2023
Subjects
Hessian矩阵
循环对抗神经网络
逆时偏移
最小二乘
Online AccessGet full text
ISSN1673-5005
DOI10.3969/j.issn.1673-5005.2023.03.006

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Abstract P631.4; 最小二乘逆时偏移成像方法因计算量巨大,限制了其大规模的工业应用.基于此,建立循环对抗神经网络表征Hessian矩阵的逆,构建逆时偏移结果和反射系数之间的映射关系.通过建立的神经网络对逆时偏移成像结果进行去模糊化处理,提高成像质量,同时大幅减少计算时间.将训练好的网络应用于Marmousi模型和Sigsbee2A模型的逆时偏移结果.结果表明,本文方法在不显著增加计算量的情况下较好地提高了逆时偏移成像质量.
AbstractList P631.4; 最小二乘逆时偏移成像方法因计算量巨大,限制了其大规模的工业应用.基于此,建立循环对抗神经网络表征Hessian矩阵的逆,构建逆时偏移结果和反射系数之间的映射关系.通过建立的神经网络对逆时偏移成像结果进行去模糊化处理,提高成像质量,同时大幅减少计算时间.将训练好的网络应用于Marmousi模型和Sigsbee2A模型的逆时偏移结果.结果表明,本文方法在不显著增加计算量的情况下较好地提高了逆时偏移成像质量.
Author 黄建平
刘博文
李振春
黄韵博
AuthorAffiliation 中国石油大学(华东)地球科学与技术学院,山东青岛 266580
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HUANG Jianping
LI Zhenchun
HUANG Yunbo
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循环对抗神经网络
逆时偏移
最小二乘
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Snippet P631.4; 最小二乘逆时偏移成像方法因计算量巨大,限制了其大规模的工业应用.基于此,建立循环对抗神经网络表征Hessian矩阵的逆,构建逆时偏移结果和反射系数之间的映射关系....
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Title 基于循环对抗神经网络的快速最小二乘逆时偏移成像方法
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