Estimation of source parameters using a non-Gaussian probability density function in a Bayesian framework

Abstract Source parameters represent key factors in seismic hazard assessment and understanding source physics of earthquakes. In addition to conventional grid search approach estimate parameters, other approaches have been used recently. This study uses a Bayesian framework, the Markov Chain Monte Carlo method, including uncertainty with inter-parameter correlations. The calculation method requires select probability density function for estimating likelihood can influence reliability. While most studies use normal distribution, we an F -distribution due its suitability data ratio form. Using synthetic real observations from induced earthquakes Oklahoma, compare steps spectral fitting parameter estimation using two functions. sampling distribution estimated support assumption that is well-suited analysis. Results further show effectively reveal trade-offs among parameters. Sampling trends also quality criteria be refine results. Graphical