Date of Award
2023
Document Type
Open Access Master's Thesis
Degree Name
Master of Science in Geophysics (MS)
Administrative Home Department
Department of Geological and Mining Engineering and Sciences
Advisor 1
Gregory Waite
Committee Member 1
Roohollah Askari
Committee Member 2
Thomas Oommen
Abstract
Pacaya volcano located 30 km SW of Guatemala City, Guatemala, has been erupting intermittently since 1961. Monitoring of seismicity is crucial to understanding current activity levels within Pacaya. Traditional methods of picking these small earthquakes in this noisy environment are imprecise. Pacaya produces many small events that can easily blend in with the background noise. A possible solution for this problem is a machine learning program to pick first arrivals for these earthquakes. We tested a deep learning algorithm (Mousavi et al., 2020) for fast and reliable seismic signal detection within a volcanic system. Data from multiple deployments were used, including permanent and temporary arrays from 2015 to 2022. Initially over 12,000 independent events were detected although most were unlocatable. A predetermined 1D velocity model calculated by Lanza & Waite (2018) was initially used to locate the earthquakes. This velocity model was updated using VELEST and the locations were calculated using new 1D P-wave and S-wave velocity models. This resulted in 512 events after a quality control filtering process. These events ranged in depths from -2.5 km (summit of Pacaya) to 0 km (sea level) all located directly beneath the vent. The detection process took about 2-3 hours per 15 days on each 3-component broadband seismometer. The method shows promise in providing an efficient and effective method to pick volcano tectonic seismic events, and it did well identifying the emergent arrivals in these datasets; however, it has shortcomings in detecting some low-frequency event types. This could be addressed through additional training of the algorithm. The very low speeds in our new P-wave and S-wave velocity models highlight the poor consolidation of the young MacKenney cone. Further study is encouraged to better understand the accuracy and type of earthquakes picked, especially the increased level of activity during or leading up to an eruption at Pacaya volcano.
Recommended Citation
DeVlieg, Jessica L., "COMPREHENSIVE ANALYSIS OF SEISMIC SIGNALS FROM PACAYA VOLCANO USING DEEP LEARNING EVENT DETECTION", Open Access Master's Thesis, Michigan Technological University, 2023.