Bayesian inference of subglacial topography using mass conservation

We develop a Bayesian model for estimating ice thickness given sparse observations coupled with estimates of surface mass balance, surface elevation change, and surface velocity. These fields are related through mass conservation. We use the Metropolis-Hastings algorithm to sample from the posterior...

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Bibliographic Details
Published in:Frontiers in Earth Science
Main Authors: Douglas John Brinkerhoff, Andy eAschwanden, Martin eTruffer
Format: Article in Journal/Newspaper
Language:English
Published: Frontiers Media S.A. 2016
Subjects:
Bayesian inference
inverse methods
Mass conservation
uncertainty quantification
continuity equation
subglacial topography
Science
Q
Hastings
Jakobshavn
Online Access:https://doi.org/10.3389/feart.2016.00008
https://doaj.org/article/b740a0b4534e424c8ddb0fbb84202b72
Description
Summary:We develop a Bayesian model for estimating ice thickness given sparse observations coupled with estimates of surface mass balance, surface elevation change, and surface velocity. These fields are related through mass conservation. We use the Metropolis-Hastings algorithm to sample from the posterior probability distribution of ice thickness for three cases: a synthetic mountain glacier, Storglaciaren, and Jakobshavn Isbrae. Use of continuity in interpolation improves thickness estimates where relative velocity and surface mass balance errors are small, a condition difficult to maintain in regions of slow flow and surface mass balance near zero. Estimates of thickness uncertainty depend sensitively on spatial correlation. When this structure is known, we suggest a thickness measurement spacing of one to two times the correlation length to take best advantage of continuity based interpolation techniques. To determine ideal measurement spacing, the structure of spatial correlation must be better quantified.

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