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Mitigating bias in inversion of InSAR data resulting from radar viewing geometries
Geophysical Journal International ( IF 2.8 ) Pub Date : 2021-06-14 , DOI: 10.1093/gji/ggab229
Quentin Dumont 1 , Valérie Cayol 1 , Jean-Luc Froger 1
Affiliation  

SUMMARY InSAR data acquired from ascending and descending orbits are often characterized by different magnitudes of the observed line-of-sight displacements, which may potentially bias inverse models. Using synthetic numerical models of dyke intrusions, we show that biased solutions are obtained when carrying out ‘conventional’ inversions where only observation and modelling errors are taken into consideration. To mitigate the impact of the relative magnitudes of the data, we propose two methods: a covariance weighting inversion and a wrapped data inversion. These methods are compared to a conventional inversion using synthetic data generated by models of dykes of known geometry. We find that the covariance weighting method allows to retrieve an initial source geometry better than the other methods. These methods are then applied to the July 2017 eruption of Piton de la Fournaise. Using a covariance weighting inversion, the difference in fit between data sets decreases from 50% to 20 % and the newly estimated source is in better agreement with the geological context.

中文翻译:

减轻由雷达观测几何引起的 InSAR 数据反演偏差

总结 从上升和下降轨道获取的 InSAR 数据通常以观察到的视线位移大小不同为特征,这可能会使反演模型产生偏差。使用堤坝侵入的合成数值模型,我们表明,在只考虑观察和建模误差的“常规”反演时,会获得有偏差的解决方案。为了减轻数据相对大小的影响,我们提出了两种方法:协方差加权反演和包裹数据反演。将这些方法与使用由已知几何形状的堤坝模型生成的合成数据进行的常规反演进行比较。我们发现协方差加权方法允许比其他方法更好地检索初始源几何。然后将这些方法应用于 2017 年 7 月的 Piton de la Fournaise 喷发。使用协方差加权反演,数据集之间的拟合差异从 50% 减少到 20%,并且新估计的来源与地质背景更一致。
更新日期:2021-06-14
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