Thread: S011: Frontiers of Uncertainty Quantification in Geoscientific Inversion

Started: 2018-07-18 10:08:23
Last activity: 2018-07-18 10:08:23
Topics: AGU Meetings
Dear Colleague,



We are excited to continue our special session on "Frontiers of uncertainty
quantification in geoscientific inversion” at the 2018 AGU Fall meeting. We
aim to provide a cross-disciplinary view of innovations in the field and be
inclusive to a wide variety of work and applications (the abstract is
copied below). We are pleased to have very broad support from the AGU
sections of Seismology, Study of Earth's Deep Interior, Geomagnetism,
Paleomagnetism and Electromagnetism, Hydrology, and Near Surface Geophysics:



https://agu.confex.com/agu/fm18/prelim.cgi/Session/52107



We are delighted to have as invited presenters, Noemi Petra (UC Merced) and
Daniela Calvetti (Case Western).



We hope that this session will be of interest to you and that you and/or
your students will consider contributing an abstract.



The abstract deadline is August 1, 2018.



Our apologies for any cross postings of this advertisement.



Best wishes,

Vedran Lekic (U Maryland)

Jan Dettmer (U Calgary)

Burke Minsley (USGS)

Thomas Bodin (U Lyon)

Kerry Key (Columbia U)

Niklas Linde (U Lausanne)

Louise Pellerin (Green Geophysics)

Anandaroop Ray (Chevron)



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S011: Frontiers of Uncertainty Quantification in Geoscientific Inversion



Geoscientists employ inversion methods to infer Earth properties and
processes. These inferences are based on observations and described by
model parameters. However, incomplete and noisy observations, subjective
processing and inversion choices, non-uniqueness, and approximate physical
theory lead to parameter uncertainty. Therefore, robust interpretation of
inversion results requires uncertainty estimation, which relates parameter
knowledge to data errors. Rigorous methods for uncertainty quantification
are of great practical value across disciplines, particularly those
involving predictions, such as probabilistic hazard assessment or
hydrologic modeling. For instance, how uncertain are inferred locations of
unexploded ordnances, economic resources, or chemical contaminants? How
uncertain are tsunami predictions given uncertain earthquake rupture and
earth structure knowledge? What role can machine learning play in
uncertainty quantification? We invite submissions on all aspects of
uncertainty quantification, including theoretical advances and practical
applications across all fields of geosciences.

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