当前位置: X-MOL 学术Geophys. J. Int. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Outlier-insensitive Bayesian inference for linear inverse problems (OutIBI) with applications to space geodetic data
Geophysical Journal International ( IF 2.8 ) Pub Date : 2019-12-12 , DOI: 10.1093/gji/ggz559
Yu Hang 1 , Sylvain Barbot 2 , Justin Dauwels 1 , Teng Wang 3 , Priyamvada Nanjundiah 4 , Qiang Qiu 2
Affiliation  

SUMMARY
Inverse problems play a central role in data analysis across the fields of science. Many techniques and algorithms provide parameter estimation including the best-fitting model and the parameters statistics. Here, we concern ourselves with the robustness of parameter estimation under constraints, with the focus on assimilation of noisy data with potential outliers, a situation all too familiar in Earth science, particularly in analysis of remote-sensing data. We assume a linear, or linearized, forward model relating the model parameters to multiple data sets with a priori unknown uncertainties that are left to be characterized. This is relevant for global navigation satellite system and synthetic aperture radar data that involve intricate processing for which uncertainty estimation is not available. The model is constrained by additional equalities and inequalities resulting from the physics of the problem, but the weights of equalities are unknown. We formulate the problem from a Bayesian perspective with non-informative priors. The posterior distribution of the model parameters, weights and outliers conditioned on the observations are then inferred via Gibbs sampling. We demonstrate the practical utility of the method based on a set of challenging inverse problems with both synthetic and real space-geodetic data associated with earthquakes and nuclear explosions. We provide the associated computer codes and expect the approach to be of practical interest for a wide range of applications.


中文翻译:

线性反问题的离群值不敏感贝叶斯推断(OutIBI)及其在空间大地测量数据中的应用

概要
逆问题在整个科学领域的数据分析中起着核心作用。许多技术和算法都提供参数估计,包括最适合的模型和参数统计信息。在此,我们关注约束条件下参数估计的鲁棒性,重点是将噪声数据与潜在离群值同化,这是地球科学中非常熟悉的一种情况,尤其是在遥感数据分析中。我们假设一个线性的或线性的正向模型,将模型参数与具有先验未知不确定性的多个数据集相关联,这些不确定性尚待表征。这与涉及复杂处理且不确定性估算不可用的全球导航卫星系统和合成孔径雷达数据有关。该模型受到问题的物理性导致的其他等式和不等式的约束,但是等式的权重未知。我们从贝叶斯的角度用无信息的先验来阐述问题。然后通过吉布斯采样推断模型参数,权重和离群值的后验分布。我们基于一组具有挑战性的反问题,结合与地震和核爆炸相关的合成和实际空间-大地数据,论证了该方法的实用性。我们提供了相关的计算机代码,并期望该方法对广泛的应用具有实际意义。我们从贝叶斯的角度用无信息的先验来阐述问题。然后通过吉布斯采样推断模型参数,权重和离群值的后验分布。我们基于一组具有挑战性的反问题,结合与地震和核爆炸有关的合成和实际空间-大地数据,论证了该方法的实用性。我们提供了相关的计算机代码,并期望该方法对广泛的应用具有实际意义。我们从贝叶斯的角度用无信息的先验来阐述问题。然后通过吉布斯采样推断模型参数,权重和离群值的后验分布。我们基于一组具有挑战性的反问题,结合与地震和核爆炸相关的合成和实际空间-大地数据,论证了该方法的实用性。我们提供了相关的计算机代码,并期望该方法对广泛的应用具有实际意义。我们基于一组具有挑战性的反问题,结合与地震和核爆炸相关的合成和实际空间-大地数据,论证了该方法的实用性。我们提供了相关的计算机代码,并期望该方法对广泛的应用具有实际意义。我们基于一组具有挑战性的反问题,结合与地震和核爆炸相关的合成和实际空间-大地数据,论证了该方法的实用性。我们提供了相关的计算机代码,并期望该方法对广泛的应用具有实际意义。
更新日期:2020-02-07
down
wechat
bug