Articles | Volume 6, issue 3
Short communication
24 Jul 2015
Short communication |  | 24 Jul 2015

Revisiting the statistical analysis of pyroclast density and porosity data

B. Bernard, U. Kueppers, and H. Ortiz

Abstract. Explosive volcanic eruptions are commonly characterized based on a thorough analysis of the generated deposits. Amongst other characteristics in physical volcanology, density and porosity of juvenile clasts are some of the most frequently used to constrain eruptive dynamics. In this study, we evaluate the sensitivity of density and porosity data to statistical methods and introduce a weighting parameter to correct issues raised by the use of frequency analysis. Results of textural investigation can be biased by clast selection. Using statistical tools as presented here, the meaningfulness of a conclusion can be checked for any data set easily. This is necessary to define whether or not a sample has met the requirements for statistical relevance, i.e. whether a data set is large enough to allow for reproducible results. Graphical statistics are used to describe density and porosity distributions, similar to those used for grain-size analysis. This approach helps with the interpretation of volcanic deposits. To illustrate this methodology, we chose two large data sets: (1) directed blast deposits of the 3640–3510 BC eruption of Chachimbiro volcano (Ecuador) and (2) block-and-ash-flow deposits of the 1990–1995 eruption of Unzen volcano (Japan). We propose the incorporation of this analysis into future investigations to check the objectivity of results achieved by different working groups and guarantee the meaningfulness of the interpretation.

Short summary
This paper presents a new methodology to treat statistically pyroclast density and porosity data sets introducing a weighting parameter. It also proposes a stability analysis to check if the sample set is large enough for statistical reliability. Finally we introduce graphical statistics to improve distinction between pyroclastic deposits and understanding of eruptive dynamics. An open source R code is supplied that includes all these features in order to facilitate data processing.