Adaptive waveform inversion: Theory
Adaptive waveform inversion: Theory
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DOI：
10.1190/geo2015-0387.1
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发表时间：
2014-08
期刊：
影响因子：
3.3
通讯作者：
M. Warner;L. Guasch
中科院分区：
文献类型：
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作者：
M. Warner;L. Guasch
传统的全波形地震反演试图找到一个能够准确预测观测地震波形的地下模型;它通过直接最小化观测数据和预测数据之间的差异，从假设的起始模型开始迭代一系列线性化步骤来进行。如果该起始模型与真实模型相距太远，则该方法导致伪模型，其中预测数据相对于观测数据被跳过周期。自适应波形反演（AWI）提供了一种新形式的全波形反演（FWI），似乎是免疫的问题，否则产生的周期跳跃。在这种方法中，最小二乘卷积滤波器的设计，将预测数据转换为观测数据。反演问题被制定为使得地下模型被迭代地更新以迫使这些维纳滤波器朝向零滞后δ函数。随着这一目标的实现，预测的数据朝着观察到的方向发展。
ABSTRACTConventional full-waveform seismic inversion attempts to find a model of the subsurface that is able to predict observed seismic waveforms exactly; it proceeds by minimizing the difference between the observed and predicted data directly, iterating in a series of linearized steps from an assumed starting model. If this starting model is too far removed from the true model, then this approach leads to a spurious model in which the predicted data are cycle skipped with respect to the observed data. Adaptive waveform inversion (AWI) provides a new form of full-waveform inversion (FWI) that appears to be immune to the problems otherwise generated by cycle skipping. In this method, least-squares convolutional filters are designed that transform the predicted data into the observed data. The inversion problem is formulated such that the subsurface model is iteratively updated to force these Wiener filters toward zero-lag delta functions. As that is achieved, the predicted data evolve toward the observed...