Modelling the spatial distribution of volcanoes: an example from Armenia
Published:January 01, 2006
J. N. Weller, A. J. Martin, C. B. Connor, L. J. Connor, A. Karakhanian, 2006. "Modelling the spatial distribution of volcanoes: an example from Armenia", Statistics in Volcanology, H. M. Mader, S. G. Coles, C. B. Connor, L. J. Connor
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Geoscientists worldwide are faced with the task of assessing hazards associated with point-like features such as volcanoes and earthquake epicentres on various temporal and spatial scales. Commonality among these phenomena exists because the analysis of their spatial distribution and geological setting can be used to estimate hazards quantitatively. Often, these geological hazard assessments must evaluate the likelihood of very infrequent events that have high consequence. For example, in the last two decades long-term probabilistic volcanic hazard assessment has been used in siting nuclear facilities (McBirney & Godoy 2003). Such assessments have been conducted at Yucca Mountain, Nevada, USA (Crowe et al. 1982; Connor et al. 2000), the Muria Peninsula, Indonesia (McBirney et al. 2003), near Yerevan, Armenia (Karakhanian et al. 2003), and in Japan (Martin et al. 2004). A central issue in all of these assessments is the likelihood of a new volcano forming by eruptions in close proximity to the facility. At such facilities, hazards with probabilities of the order of 10−6 − 10−8 per year are often considered high (Connor & Hill 1995; Martin et al. 2004) because overall the risks associated with such facilities must be very low.Similar assessments are used in Auckland, New Zealand, to estimate the probability of new volcanoes or volcanic vents forming in urban centres built in regions of active volcanism (e.g. Magill Blong 2005), and are used to forecast the location of new lava ï—‚ow vents on large volcanoes, such as Mt.
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Statistics in Volcanology
Statistics in Volcanology is a comprehensive guide to modern statistical methods applied in volcanology written by today's leading authorities. The volume aims to show how the statistical analysis of complex volcanological data sets, including time series, and numerical models of volcanic processes can improve our ability to forecast volcanic eruptions. Specific topics include the use of expert elicitation and Bayesian methods in eruption forecasting, statistical models of temporal and spatial patterns of volcanic activity, analysis of time series in volcano seismology, probabilistic hazard assessment, and assessment of numerical models using robust statistical methods. Also provided are comprehensive overviews of volcanic phenomena, and a full glossary of both volcanological and statistical terms.
Statistics in Volcanology is essential reading for advanced undergraduates, graduate students, and research scientists interested in this multidisciplinary field.