Date of Award

Winter 12-2016

Embargo Period

1-10-2017

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Civil and Environmental Engineering

Advisor(s)

Mitchell J. Small

Second Advisor

Matteo Pozzi

Third Advisor

William Harbert

Abstract

The wide application of fluid injection has caused a concern of the potential critical risk associated with induced seismicity. To help clarify the concern, this dissertation proposes a statistical approach for assessing seismic transitions associated with fluid injections by scientifically analyzing instrumental measures of seismic events. The assessment problem is challenging due to the uncertain effects of wastewater injections on regional seismicity, along with the limited availability of seismic and injection data. To overcome the challenge, three statistical methods are developed, with each being focused on a different aspect of the problem. Specifically, a statistical method is developed for early detection of induced seismicity, with the potential of allowing for site managers and regulators to act promptly and preparing communities for the increased seismic risk; the second method aims for addressing the further need of quantitatively assessing the transition of induced seismicity, which can reveal the underlying process of induced seismicity and provide data to support probabilistic seismic hazard analysis; and finally, the third method steps further to characterize the process of spatial distribution of induced seismicity, which accounts for spatial evolution of induced seismicity. All the proposed methods are built on the principles of Bayesian technique, which provides a flexible inference framework to incorporate domain expertise and data uncertainty. The effectiveness of the proposed methods is demonstrated using the earthquake dataset for the state of Oklahoma, which shows a promising result: the detection method is able to issue warning of induced seismicity well before the occurrence of severe consequences; the transition model provides a significantly better fit to the dataset than the classical model and sheds light on the underlying transition of induced seismicity in Oklahoma; and the spatio-temporal model provides a most comprehensive characterization of the dataset in terms of its spatial and temporal properties and is shown to have a much better short-term forecasting performance than the “naïve methods”. The proposed methods can be used in combination as a decision-making support tool to identify areas with increasing levels of seismic risk in a quantitative manner, supporting a comprehensive assessment to decide which risk-mitigation strategy should be recommended.

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