An efficient step-length formula for correlative least-squares reverse time migration
An efficient step-length formula for correlative least-squares reverse time migration
复制标题
相关最小二乘逆时偏移的高效步长公式
DOI：
10.1190/geo2015-0529.1
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发表时间：
2016-05
期刊：
影响因子：
3.3
通讯作者：
José Badal
中科院分区：
文献类型：
--
作者：
Liu Youshan;Teng Jiwen;Xu Tao;Bai Zhiming;Lan Haiqiang;José Badal
在相关最小二乘逆时偏移(CLSRTM)中，最佳步长的估计通常是通过拟合抛物线并求其最小值来确定的，这涉及到至少两次额外读取所有地震记录，这大大降低了算法的效率。为了提高CLSRTM算法的效率，我们根据去偏移算子的线性性质，推导了一个解析步长公式。用Marmousi模型和Sigsbee2A型模型合成的数据进行了数值算例验证。在具有不完全迁移速度的复杂模型中，例如Sigabee2A型模型，我们的公式使目标函数的值收敛到一个小得多的最小值。对不定期获取的数据或被不同噪声水平污染的数据进行了额外的数值测试，验证了ASL公式的稳健性。与常用的抛物线搜索法相比，ASL公式具有更大的优越性。
ABSTRACTIn correlative least-squares reverse time migration (CLSRTM), the estimation of the optimal step size is usually determined by fitting a parabola and finding its minimum; it involves at least two times extra reading of all seismic records, which significantly lowers the efficiency of the algorithm. To improve the efficiency of the CLSRTM algorithm, we have deduced an analytical step-length (ASL) formula based on the linear property of the demigration operator. Numerical examples performed with the data synthetized by the Marmousi and Sigsbee2A models were used to test its validity. In complex models with imperfect migration velocity, such as the Sigabee2A model, our formula makes the value of the objective function converges to a much smaller minimum. Additional numerical tests performed with the data either acquired irregularly or contaminated by different noise levels verify the robustness of the ASL formula. Compared with the commonly used parabolic search method, the ASL formula is much more e...
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影响因子：
3.3
作者：
J. Etgen;S. Gray;Yu Zhang
通讯作者：
J. Etgen;S. Gray;Yu Zhang
DOI：
--
发表时间：
2005
期刊：
--
影响因子：
--
作者：
Marie L. Prucha
通讯作者：
Marie L. Prucha
影响因子：
3.3
作者：
B. Duquet;K. Marfurt;J. Dellinger
通讯作者：
B. Duquet;K. Marfurt;J. Dellinger
影响因子：
3.3
作者：
Liu, Yike;Chang, Xu;Zheng, Yingcai
通讯作者：
Zheng, Yingcai
影响因子：
3.3
作者：
Hill, NR
通讯作者：
Hill, NR