Determination of the largest clast sizes of tephra deposits for the characterization of explosive eruptions: a study of the IAVCEI commission on tephra hazard modelling

摘要

The distribution of clasts deposited around a volcano\udduring an explosive eruption typically contoured by\udisopleth maps provides important insights into the associated\udplume height, wind speed and eruptive style. Nonetheless,\uda wide range of strategies exists to determine the largest\udclasts, which can lead to very different results with obvious\udimplications for the characterization of eruptive behaviour\udof active volcanoes. The IAVCEI Commission on Tephra\udHazard Modelling has carried out a dedicated exercise to\udassess the influence of various strategies on the determination\udof the largest clasts. Suggestions on the selection of\udsampling area, collection strategy, choice of clast typologies\udand clast characterization (i.e. axis measurement and\udaveraging technique) are given, mostly based on a thorough\udinvestigation of two outcrops of a Plinian tephra deposit from\udCotopaxi volcano (Ecuador) located at different distances\udfrom the vent. These include: (1) sampling on a flat paleotopography\udfar from significant slopes to minimize remobilization\udeffects; (2) sampling on specified-horizontal-area sections\ud(with the statistically representative sampling area depending\udon the outcrop grain size and lithic content); (3) clast characterization\udbased on the geometric mean of its three orthogonal\udaxes with the approximation of the minimum ellipsoid (lithic\udfragments are better than pumice clasts when present); and\ud(4) use of the method of the 50th percentile of a sample of 20\udclasts as the best way to assess the largest clasts. It is also\udsuggested that all data collected for the construction of\udisopleth maps be made available to the community through\udthe use of a standardized data collection template, to assess the\udapplicability of the new proposed strategy on a large number\udof deposits and to build a large dataset for the future development\udand refinement of dispersal models.