Thesis advisor: John E. Ebel / Large magnitude offshore passive margin earthquakes are rare, making small magnitude events (M < 4) the predominant data available to study the mechanisms of seismicity along passive margins. This study is focused on a swarm of events (M2.1-M3.9) that occurred from 2012-2013 located in the Gulf of Maine (GM) along the Atlantic Passive Margin (APM) shelf break, a region with previously minimal recorded seismic activity. Relative locations were calculated for the earthquakes of the GM swarm and a moment tensor inversion method was used to calculate focal mechanisms for the two largest events in the swarm. The results of the relative location method constrained a fault orientation to a strike of 243° ± 3° and a dip of 25° ± 3°. The focal mechanisms for the two largest events were determined to be oblique normal faults with steeply dipping planes at depths between 12-18 km. For the largest event (M3.9), the strike is 235° ± 1°, with a dip of 77.7° ± .8° and a rake of -116.5° ± 3°, and for the second largest event (M3.7) the strike is 259° ± 3°, with a dip of 78° ± 2° and a rake of -58.8° ± 7°. By mapping the spatial extent of the relative hypocenters, I infer a potential fault size of 2.7 km by 2.4 km. If this entire area were to rupture at once in the future, an earthquake of M4.9-M5.0 could occur, a magnitude not large enough to be tsunamigenic in the GM. Based on Gutenberg-Richter relations from the eastern APM, if a M7 can occur in the GM, its estimated mean repeat time is 2,120-22,800 years, and it could be tsunamigenic depending on the event’s proximity to the continental slope. / Thesis (MS) — Boston College, 2016. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Earth and Environmental Sciences.

This thesis proposes a framework for identifying the root-cause of a voltage disturbance, as well as, its source location (upstream/downstream) from the monitoring place. The framework works with three-phase voltage and current waveforms collected in radial distribution networks without distributed generation. Real-world and synthetic waveforms are used to test it. The framework involves features that are conceived based on electrical principles, and assuming some hypothesis on the analyzed phenomena. Features considered are based on waveforms and timestamp information. Multivariate analysis of variance and rule induction algorithms are applied to assess the amount of meaningful information explained by each feature, according to the root-cause of the disturbance and its source location. The obtained classification rates show that the proposed framework could be used for automatic diagnosis of voltage disturbances collected in radial distribution networks. Furthermore, the diagnostic results can be subsequently used for supporting power network operation, maintenance and planning. / En esta tesis se propone una metodología para la identificación de la localización relativa (aguas arriba/abajo) y la causa de una perturbación eléctrica. La metodología utiliza las ondas trifásicas de tensión y de corriente registradas en redes de distribución radial sin presencia de generación distribuida. La metodología es validada utilizando perturbaciones eléctricas reales y simuladas. La metodología involucra atributos que han sido concebidos basándose en principios eléctricos e hipótesis de acuerdo a cada uno de los fenómenos eléctricos analizados. Se propusieron atributos tanto basados en la forma de onda como en la fecha de ocurrencia de la perturbación. La cantidad de información contenida y/o explicada por cada atributo es valorada mediante la aplicación del análisis multivariante de la varianza y algoritmos de extracción automática de reglas de decisión. Los resultados de clasificación muestran que la metodología propuesta puede ser utilizada para el diagnóstico automático de perturbaciones eléctricas registradas en redes de distribución radial. Los resultados de diagnóstico pueden ser utilizados para apoyar las tareas de operación, mantenimiento y planeamiento de las redes de distribución.