Improving local mean stress estimation using Bayesian hierarchical modelling

Author(s):  
Yu Feng ◽  
Ke Gao ◽  
Arnaud Mignan ◽  
Jiawei Li
2021 ◽  
Author(s):  
Yann Ziegler ◽  
Bramha Dutt Vishwakarma ◽  
Aoibheann Brady ◽  
Stephen Chuter ◽  
Sam Royston ◽  
...  

<p>Glacial Isostatic Adjustment (GIA) and the hydrological cycle are both associated with mass changes, which are observed by GRACE, and vertical land motion (VLM), which is observed by GPS. Hydrology-related VLM results from the instantaneous response of the elastic solid Earth to surface loading by freshwater, whereas GIA-related VLM reveals the long-term response of the visco-elastic Earth mantle to past glacial cycles. Thus, observations of mass changes and VLM are interrelated and GIA and hydrology are difficult to investigate independently. Taking advantage of the differences in the spatio-temporal characteristics of the GIA and hydrology fields, we can separate the respective contributions of each process. In this work, we use a Bayesian Hierarchical Modelling (BHM) approach to provide a new data-driven estimate of GIA and time-evolving hydrology-related VLM for North America. We detail our processing strategy to prepare the input data for the BHM while preserving the content of the original observations. We discuss the separation of GIA and hydrology processes from a statistical and geophysical point of view. Finally, we assess the reliability of our estimates and compare our results to the latest GIA and hydrological models. Specifically, we compare our GIA solution to a forward-model global field, ICE-6G, and a recent GIA estimate developed for North America (Simon et al. 2017). Our time-evolving hydrology field is compared with WaterGAP, a global water balance model. Overall, for both GIA and hydrology, there is a good agreement between our results and the forward models, but we also find differences which possibly highlight deficiencies in these models.</p>


2019 ◽  
Vol 15 (S352) ◽  
pp. 114-114
Author(s):  
Emma Curtis-Lake

AbstractThe mass-SFR relation of galaxies encodes information of present and historical star formation in the galaxy population. We expect the intrinsic scatter in the relation to increase to low mass where SFR becomes more stochastic. Measurements at z ‰ 4 from the Hubble Frontier fields have hinted at this (Santini et al., 2017), however, with the added uncertainty of lensing magnification we await JWST to provide robust measurements. Even with data-sets provided by JWST, uncertainties on mass and SFR estimates are often large, potentially covariant and dependent on assumptions used. I will present our method of Bayesian hierarchical modelling of the mass-SFR relation that self-consistently propagates uncertainties on mass and SFR estimates to uncertainties on the mass-SFR relation parameters. I will expose the biases imposed by standard SED-modelling practices, and address to what significance we can measure an increase in intrinsic scatter to low masses with JWST.


PLoS Medicine ◽  
2020 ◽  
Vol 17 (2) ◽  
pp. e1003026 ◽  
Author(s):  
Vladimíra Kantorová ◽  
Mark C. Wheldon ◽  
Philipp Ueffing ◽  
Aisha N. Z. Dasgupta

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