Date of Graduation
5-2019
Document Type
Thesis
Degree Name
Master of Science in Statistics and Analytics (MS)
Degree Level
Graduate
Department
Statistics and Analytics
Advisor/Mentor
John R. Tipton
Committee Member
Mark Arnold
Second Committee Member
Giovanni Petris
Keywords
Autoregressive Model Parameter, Bayesian, Change Point Model, Parallel Computing, Prediction, Real-time Estimation, Seismic, Uncertainty
Abstract
Because earthquakes have a large impact on human society, statistical methods for better studying earthquakes are required. One characteristic of earthquakes is the arrival time of seismic waves at a seismic signal sensor. Once we can estimate the earthquake arrival time accurately, the earthquake location can be triangulated, and assistance can be sent to that area correctly. This study presents a Bayesian framework to predict the arrival time of seismic waves with associated uncertainty. We use a change point framework to model the different conditions before and after the seismic wave arrives. To evaluate the performance of the model, we conducted a simulation study where we could evaluate the predictive performance of the model framework. The results show that our method has acceptable performance of arrival time prediction with accounting for the uncertainty.
Citation
Zhong, H. (2019). A Bayesian Framework for Estimating Seismic Wave Arrival Time. Graduate Theses and Dissertations Retrieved from https://scholarworks.uark.edu/etd/3282
Included in
Applied Statistics Commons, Geology Commons, Geophysics and Seismology Commons, Longitudinal Data Analysis and Time Series Commons, Statistical Models Commons, Survival Analysis Commons, Tectonics and Structure Commons