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\chapter{Context}  \section{Introduction}  The presence of induced seismicity at geothermal power generation sites has been recognized for decades \cite{Ward_1972} \cite{Allis_1982}. Commonly these events are caused by temperature and pressure change within a reservoir as a result of fluid injection, although seismicity has also been attributed to reservoir volume changes and changes in fluid chemistry \cite{Allis_1982} \cite{Sherburn_2015}. Most of this seismicity is of magnitude \textless 3.0, termed microseismicity, and normally presents limited hazard to local population and infrastructure, although the degree to which humans are affected varies considerably from location to location \cite{cladouhos2010injection}. Importantly, microseismicity can provide very useful information about the movement of fluid and pressure within the reservoir as well as size and distribution of the reservoir fracture system and therefore has implications for geothermal resource management. However, the small magnitude of the events, high levels of anthropogenic noise and, potentially, highly attenuating geology often make detecting such events difficult. One way to address these difficulties is to use a matched filter detection technique.  Matched filter earthquake detection uses waveform cross-correlation between continuous seismic data and known earthquake recordings to identify additional events in a seismic catalogue. Correlation-based detection offers improved performance over traditional, amplitude-based techniques due to its ability to detect signals in noisy data and when multiple multiple, repetitive  events are closely spaced in time. time \cite{Gibbons_2006}.  This significantly increases the number of events detected without increasing the rate of false detections. These advantages make matched filter detection ideal for monitoring microseismicity in areas of geothermal power generation, which are characterized by numerous noise sources and the possibility for dense clusters of small-magnitude, induced seismic events. Microseismicity at geothermal areas can be highly repetitive in both its triggering process and its spatial extent. It is therefore suited to detection via matched filtering, which is recognized to be one The purpose  of the best ways to identify near-repeating signals in continuous data \cite{Gibbons_2006}. It this study  isalso suited  toearthquake detection in noisy geothermal power generation areas because it relies on signal cross-correlation as opposed to relative amplitudes to search for events \cite{Shelly_2007}. We  investigate the performance of matched filtering on a nearly year-long four-year  dataset for Ngatamariki and Rotokawa geothermal fields on the north island of New Zealand, focusing on amplifying increasing  the number of detections triggered using standard methods and assessing what any additional detections might contribute to our knowledge of the processes at play within the reservoirs. The specific objectives are as follows:  \section{Objectives}  \begin{itemize}