VOLCANO: IAVCEI 2017 Session VIII.1 New approaches using statistical methods in volcanology

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From: "Richardson, Jacob A. (GSFC-698.0)[UNIVERSITIES SPACE RESEARCH ASSOCIATION]" <jacob.a.richardson@xxxxxxxx>
Subject: IAVCEI 2017 Session VIII.1 New approaches using statistical methods in volcanology
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Dear colleagues,

 


We invite you to consider submitting an abstract to our IAVCEI session VIII.1 New approaches using statistical methods in volcanology. This session is sponsored by the Commission on Statistics in Volcanology.

 

The 2017 IAVCEI Scientific Assembly takes place 14-18 August 2017 in Portland, OR, USA. Abstracts can be submitted at http://iavcei2017.org/abstract.html and are due 17 March 2017.

 

 

VIII.1 New approaches using statistical methods in volcanology:


Statistical methods are central to robust interpretation of volcanological datasets. Such methods hold great promise, both for studying active processes where large quantities of multi-instrumental data are becoming available, and for past eruptions where community databases are becoming more complete and far-reaching. Data from observations derived above or below the surface provide different perspectives that, with proper analysis, can be merged for a more complete view of a volcano and its eruptive behavior. Statistical methods are used to evaluate data quality (including quantifying uncertainty and data coherence), interpolate over data gaps, detect changes, merge multiple datasets, correlate observations, etc. Such methods, combined with predictive and physics-based models, represent a link between modern processes and the geologic record that has the potential to integrate traditionally distinct disciplines. However, significant challenges remain due to the episodic nature of volcanism and the variety, completeness, and quality of data. We welcome contributions from any field of volcanology that emphasizes statistical analysis. We particularly seek contributions that use statistics to compare geochemistry, geophysics, physical volcanology, petrology, remote sensing, topography, InSAR, and other observational datasets with predictions from models to investigate the rich magmatic histories that lead to volcanic eruptions.

 

 

We are 99.7% confident that this session will be significant to your conference, whether you’re a Bayesian or a Frequentist.

 


 

Sincerely, the Conveners:


Gabor Kereszturi, Massey University, NZ, kereszturi_g@xxxxxxxxx

Leif Karlstrom, University of Oregon, USA, leif@xxxxxxxxxxx

Benjamin Black, City College of New York, USA, bblack.ccny@xxxxxxxxx

Jacob Richardson, NASA Goddard Space Flight Center, USA, jacob.a.richardson@nasa.gov






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