2017 Fall Meeting

11-15 December

Geoscientists employ inversion methods to infer Earth properties and processes. These inferences are based on earth 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? To produce quantitative answers, inversion methods must account for observational errors, model limitations and reduce subjective choices. We invite submissions on all aspects of uncertainty quantification, including theoretical advances and practical applications across all fields of geosciences.