HR: 09:20h
AN: V21C-06    [Abstracts]
TI: A Tephra Database With an Intelligent Correlation System, Mono-Inyo Volcanic Chain, CA
AU: * Bursik, M
EM: mib@geology.buffalo.edu
AF: University at Buffalo, SUNY, Department of Geology University at Buffalo, Buffalo, NY 14260 United States
AU: Rogova, G
EM: rogova@rochester.rr.com
AF: Encompass Consulting, Encompas Consulting, Honeoye Falls, NY 14260 United States
AB: We are assembling a web-accessible, relational database of information on past eruptions of the Mono-Inyo volcanic chain, eastern California. The PostgreSQL database structure follows the North American Data Model and CordLink. The database allows us to extract the features diagnostic of particular pyroclastic layers, as well as lava domes and flows. The features include depth in the section, layer thickness and internal stratigraphy, mineral assemblage, major and trace element composition, tephra componentry and granulometry, and radiocarbon age. Our working hypotheses are that 1) the database will prove useful for unraveling the complex recent volcanic history of the Mono-Inyo chain 2) aided by the use of an intelligent correlation system integrated into the database system. The Mono-Inyo chain consists of domes, craters and flows that stretch for 50 km north-south, subparallel to the Sierran range front fault system. Almost all eruptions within the chain probably occurred less than 50,000 years ago. Because of the variety of magma and eruption types, and the migration of source regions in time and space, it is nontrivial to discern patterns of behaviour. We have explored the use of multiple artificial neural networks combined within the framework of the Dempster-Shafer theory of evidence to construct a hybrid information processing system as an aid in the correlation of Mono-Inyo pyroclastic layers. It is hoped that such a system could provide information useful to discerning eruptive patterns that would otherwise be difficult to sort and categorize. In a test case on tephra layers at known sites, the intelligent correlation system was able to categorize observations correctly 96% of the time. In a test case with layers at one unknown site, and using a pairwise comparison of the unknown site with the known sites, a one-to-one correlation between the unknown site and the known sites was found to sometimes be poor. Such a result could be used to aid a stratigrapher in rethinking or questioning a proposed correlation. This rethinking might not happen without the input from the intelligent system.
UR: http://www.volcano.buffalo.edu/mmvz
DE: 8404 Ash deposits
DE: 8409 Atmospheric effects (0370)
DE: 8494 Instruments and techniques
SC: Volcanology, Geochemistry, Petrology [V]
MN: 2004 AGU Fall Meeting