Graduation Year


Document Type




Degree Name

Master of Science (M.S.)

Degree Granting Department


Major Professor

Charles B. Connor, Ph.D.

Committee Member

Paul H. Wetmore, Ph.D.

Committee Member

Aurélie Germa, Ph.D.


Eruption Probability, Volcanic Risk Assessment, Python, Cerro Negro Volcano, Cima Volcanic Field, Coso Volcanic Field, Southern Nevada Volcanic Field, and Arsia Mons, Mars


Recurrence rate is often used to describe volcanic activity. There are numerous documented ex- amples of non-constant recurrence rate (e.g. Dohrenwend et al., 1984; Condit and Connor, 1996; Cronin et al., 2001; Bebbington and Cronin, 2011; Bevilacqua, 2015), but current techniques for calculating recurrence rate are unable to fully account for temporal changes in recurrence rate. A local–window recurrence rate model, which allows for non-constant recurrence rate, is used to calculate recurrence rate from an age model consisting of estimated ages of volcanic eruption from a Monte Carlo simulation. The Monte Carlo age assignment algorithm utilizes paleomagnetic and stratigraphic information to mask invalid ages from the radiometric date, represented as a Gaussian probability density function. To verify the age assignment algorithm, data from Heizler et al. (1999) for Lathrop Wells is modeled and compared. Synthetic data were compared with expected results and published data were used for cross comparison and verification of recurrence rate and volume flux calculations. The latest recurrence rate fully constrained by the data is reported, based upon data provided in the referenced paper: Cima Volcanic Field, 33 +55/-14 Events per Ma (Dohren- wend et al., 1984), Cerro Negro Volcano, 0.29 Events per Year (Hill et al., 1998), Southern Nevada Volcanic Field, 4.45 +1.84/-0.87 (Connor and Hill, 1995) and Arsia Mons, Mars, 0.09 +0.14/-0.06 Events per Ma (Richardson et al., 2015). The local–window approach is useful for 1) identifying trends in recurrence rate and 2) providing the User the ability to choose the best median recurrence rate and 90% confidence interval with respect to temporal clustering.

Included in

Geology Commons