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Comparison of Frequency-Selection Strategies for 2D Frequency-Domain Acoustic Waveform Inversion

机译:二维频域声波形反演的选频策略比较

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摘要

Several frequency-selection strategies have been used to obtain global minimum solutions in waveform inversion. One strategy, called the discretization method, is to discretize frequencies with a large sampling interval to minimize redundancy in wavenumber information. Another method, the grouping method, groups frequencies with redundancy in wavenumber information. The grouping method can be carried out in two ways. With the first method, the minimum frequency is fixed and the maximum frequency is gradually extended upward (i.e., the overlap-grouping method). Under the second method, frequencies are not overlapped across the groups and waveform inversion proceeds from lower to higher frequency groups (i.e., the individual-grouping method). In this study, we compare these three frequency-selection strategies using both synthetic and real data examples based on logarithmic waveform inversion. Numerical examples for synthetic and real field data demonstrate that the three frequency-selection methods provide solutions closer to the global minimum compared to solutions resulting from simultaneously performed waveform inversion, and that the individual-grouping method yields slightly better resolution for the velocity models than the other methods, particularly for the deeper part. These results may imply that using either too small or too large data sets at every stage slightly deteriorates inversion results, and that grouping data in appropriately sized aggregations improves inversion results.
机译:几种频率选择策略已用于获得波形反演中的全局最小解。一种称为离散化方法的策略是将具有大采样间隔的频率离散化,以最大程度地减少波数信息中的冗余。另一种方法是分组方法,将波数信息中的频率进行冗余分组。分组方法可以以两种方式进行。对于第一种方法,最小频率是固定的,而最大频率是逐渐向上扩展的(即重叠分组方法)。在第二种方法下,频率在各组之间不重叠,并且波形反转从较低频率组向较高频率组进行(即,个体分组方法)。在这项研究中,我们使用基于对数波形反演的合成和实际数据示例比较了这三种频率选择策略。合成和实际数据的数值示例表明,与同时执行波形反演所得结果相比,这三种频率选择方法提供的解更接近于全局最小值,并且单独分组的方法对速度模型的分辨率比对速度的分辨率稍好。其他方法,尤其是对于较深的部分。这些结果可能意味着在每个阶段使用太小或太大的数据集都会稍微降低反演结果,并且将数据分组为适当大小的聚合会改善反演结果。

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