Consistency regions in non-linear inversion
Consistency regions in non-linear inversion
复制标题
非线性反演中的一致性区域
DOI：
10.1111/j.1365-246x.2004.02272.x
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发表时间：
2004
期刊：
影响因子：
--
通讯作者：
B. Kennett
中科院分区：
文献类型：
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作者：
B. Kennett
通过利用模型空间的性质，完全非线性反演方法的缺点是缺乏用于误差评估的良好开发的框架。为了纠正这个问题，一个辅助加权函数的合奏属性，可以使用合适的阈值来定义合适的模型的一致性区域。这种方法既不需要失配分布的详细知识，也不需要潜在的概率模型。这种一致性区域的多面体表示的使用示出了一个例子，从非线性地震事件的位置，示出了不同的选择的错配措施的效果。辅助权重函数可以直接与用于驱动非线性反演中的模型空间的探索的复合失配度量一起使用。这样的复合测量通常结合联合收割机的数据失配和正则化项。然而，通过存储每个研究模型的数据失配和相关模型特征以及复合度量可以获得相当大的益处。然后，可以回顾性地使用模型集合的属性，以通过数据失配中的一致性区域与受期望的模型属性约束的区域的交集来定义优选的模型。
SUMMARY A disadvantage of fully non-linear methods of inversion, through exploitation of the properties of model space, is the absence of a well-developed framework for error assessment. To rectify this problem an auxiliary weighting function for ensemble properties is introduced that can be used with suitable thresholds to define consistency regions of suitable models. This approach requires neither a detailed knowledge of the misfit distribution nor an underlying probabilistic model. The use of a polyhedral representation of such consistency regions is illustrated with an example from non-linear seismic event location, showing the effect of different choices for the misfit measure. The auxiliary weight function can be used directly with the composite misfit measure used to drive the exploration of model space in the non-linear inversion. Such composite measures usually combine a data misfit and regularization term. However, considerable benefit can be obtained by storing the data misfit and associated model characteristics for each investigated model, as well as the composite measure. The properties of the model ensemble can then be used retrospectively to define preferred models by the intersection of a consistency region in data misfit with zones constrained by desirable model properties.