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Learning under uncertainty for interpreting the pattern of volcanic eruptions

  • SUNY Buffalo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

The overall goal of the research presented in this paper is to design an intelligent system to aid geologists in processing complex rock characteristics for interpreting eruption patterns, and thereby to aid eruption forecasting for volcanic chains and fields. The objective of this paper is to introduce a belief-based partially supervised classification method designed to deal with high uncertainty of geological data. A case study developed to show the feasibility of the presented method for correlation of tephra layers based on geochemical characteristics is also described. This method is not specific to geological data and can be used in other applications.

Original languageEnglish
Title of host publication2015 18th International Conference on Information Fusion, Fusion 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages375-382
Number of pages8
ISBN (Electronic)9780982443866
StatePublished - Sep 14 2015
Event18th International Conference on Information Fusion, Fusion 2015 - Washington, United States
Duration: Jul 6 2015Jul 9 2015

Publication series

Name2015 18th International Conference on Information Fusion, Fusion 2015

Conference

Conference18th International Conference on Information Fusion, Fusion 2015
Country/TerritoryUnited States
CityWashington
Period07/6/1507/9/15

Keywords

  • belief functions
  • descision fusion
  • geochemical data
  • tephra
  • the Transferable Belief Model
  • uncertainty
  • volcanic eruptions

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