rowanc1/phd-thesis

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

by Archa Rowan B. Cockett

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.

Building the Thesis

Install the Curvenote CLI and run:

curvenote start

The thesis uses a GitHub Action to deploy the content to https://phd.row1.ca