Short-term hazard analysis in the presence of induced seismicity

Earthquakes could be caused by various human activities, including mining, withdrawal and injection of fluids underground, and impoundment of reservoirs. They have attracted the public's attention in the last decade due to the increasing number of earthquakes in the central and eastern U.S. The increasing induced seismicity emphasizes the importance of hazard assessment to assist risk management and decision-making during the operation. Probabilistic seismic hazard analysis has been widely used to quantify the hazard for natural earthquakes. However, its assumption of Poissonian occurrence is not valid for induced seismicity where the earthquake occurrence varies significantly in time and space due to human activities. This dissertation aims to capture and quantify the changing hazard for induced seismicity, focusing on the short-term felt shaking hazard. The declustered earthquake catalog is often used as the input for probabilistic hazard analysis models. We first evaluate four of the most popular declustering algorithms for annual induced seismic hazard analysis in Oklahoma and Kansas. We show that the choice of different declustering algorithms has significant impacts on the induced seismic hazard analysis. The algorithm by Gardner and Knopoff (1974) removes so many earthquakes that it fails to reflect the changing seismic hazard in the Oklahoma-Kansas region in the past few years. We suggest that algorithms by Reasenberg (1985) and Zaliapin and Ben-Zion (2013) can both capture the changing hazard level in the region while removing dependent earthquakes. This dissertation then introduces two frameworks to quantify short-term hazards for regions with induced seismicity, focusing on hydraulic-fracturing-induced earthquakes in West Texas and wastewater-disposal-induced earthquakes in the Oklahoma-Kansas region. These induced earthquakes differ significantly, so two separate frameworks are developed. For hydraulic-fracturing-induced earthquakes, we develop a method to estimate the hazard level at the production site during the injection, based on past injection and earthquake records. We compare the above frameworks with natural earthquakes and conclude that drivers of short-term seismic hazard vary for different seismicities. For hydraulic-fracturing-induced earthquakes in West Texas, earthquakes clustered around the injection wells dominate the short-term hazard level at production sites. For wastewater-disposal-induced earthquakes, the Poissonian mainshock rate contributes significantly to the short-term hazard level. For natural earthquakes, the aftershock sequences could be crucial. The hazard analysis for hydraulic-fracturing-induced earthquakes shows that small-magnitude earthquakes are important for the felt shaking hazard. Thus, we evaluate the performance of two existing intensity prediction equations (IPEs) for small-magnitude earthquakes at close distances and explore the impact of those earthquakes on felt shaking hazard based on "Did You Feel It (DYFI)" reports and ground motion records in the central U.S. We first compare the DYFI data with ground motion records to ensure that the former is a robust and reliable source to evaluate IPEs. Compared with IPEs, we observe that DYFI reports' intensities attenuate faster, especially for hypocentral distances beyond 10 km. Though the two IPEs do not consider soil conditions, we also explore its effect on intensities and observe that intensities at soft sites are consistently higher than intensities at stiff sites. We then generate a new IPE based on the observed data and perform hazard disaggregation to study the importance of small-magnitude earthquakes on felt shaking. Small magnitude earthquakes at close distances contribute significantly to the hazard of felt shaking. We quantify the seismic hazard after shut-in for hydraulic-fracturing-induced earthquakes. We explore different statistical models to describe the post-shut-in seismicity according to induced earthquakes in Guy-Greenbrier, Arkansas. While the seismicity usually declines after shut-in, there are cases where the seismicity surges after shut-in. We then conduct hazard assessments with and without considering post-shut-in seismicity. Results show that the post-shut-in hazard could impact decision-making significantly. We also propose a logic tree model to consider the uncertainty in model parameters and the possibility of increasing post-shut-in seismicity

Advisor: Baker Jack W. 
Beroza Gregory C. (Gregory Christian)
Kiremidjian Anne S. (Anne Setian)