Scientific Computation Seminar: Quantifying geological model structural uncertainty using airborne geophysical data

Dr. Burke Minsley from the U.S. Geological Survey

Description:

Geophysical data are typically used to infer a single ‘best’ model consistent with observations and prior information. However, because of non-uniqueness, limited resolution, and data errors, many models satisfy both the data and reasonable prior assumptions. Instead of seeking to describe the properties of any single model, a trans-dimensional Bayesian Markov chain Monte Carlo (McMC) algorithm is developed for uncertainty quantification of airborne electromagnetic (AEM) surveys. After introducing the basic role of AEM surveys for large-scale subsurface mapping, I will discuss the mechanics of a McMC algorithm developed for AEM data, along with examples where this algorithm has been used to add new insight into model uncertainty and geological interpretations. Specific aspects of the algorithm that will be discussed include: the trans-dimensional nature of the program, which allows the dimensionality of the problem to be a free parameter; the capability to assess random and/or systematic data errors as unknown parameters; the use of parallel computing tools to run multiple chains for a single dataset in order to assess convergence, and to analyze many datasets simultaneously; the use of stochastic Newton sampling to optimize sampling efficiency; and the ability to integrate multiple data types to probabilistically assess geological or hydrological properties directly.