PhD Thesis

A Framework for Geophysical Inversions

with application to vadose zone parameter estimation

Rowan Cockett

A framework for geophysical inversions
with application to vadose zone parameter estimation

by

Archa Rowan B. Cockett
B.Sc. Applied and Environmental Geology, University of Calgary, 2010

A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF

Doctor of Philosophy

in

THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES
(Geophysics)

The University of British Columbia
(Vancouver)

December 2017

© Archa Rowan B. Cockett, 2017


Lay Abstract
Geophysical methods gather data remotely to enable insights into subsurface structure and processes (e.g. locating economic resources or monitoring environmental changes). The information derived from geophysical methods is of crucial importance in resource exploration, environmental remediation, and the study of deep-earth processes. Interpretation of geophysical data requires a combination of numerical simulation and inversion. Inversion is a procedure for using data to estimate an image or model of the earth (this is similar to medical imaging). Increasingly, geoscientists are tackling complex problems that require integration of multiple types of information in order to better characterize the subsurface. In hydrogeology and geophysics, this quantitative integration requires advances in both disciplines, as well as a framework for this collaboration. The objective of this dissertation is to identify and refine a computational framework that enables and encourages sustained cross-disciplinary communication, which is a necessary step in integrated geophysical simulation research.


Keywords

  • inverse problems
  • geophysics
  • richards equation
  • vadose zone

Contributions

  • a conceptual organization and synthesis of geophysical simulations and inversions into a framework that has been rigorously, numerically tested; and
  • an algorithm for large-scale vadose zone parameter estimation for any distributed hydraulic parameter, regardless of the empirical relationship used.

PhD Defense