2015 GSA Annual Meeting in Baltimore, Maryland, USA (1-4 November 2015)

Paper No. 30-8
Presentation Time: 9:00 AM-5:30 PM


SAGINOR, Ian, Natural Science and Mathematics, Keystone College, One College Green, La Plume, PA 18440, ian.saginor@keystone.edu

Reducing risk from volcanic eruptions requires effective communication in addition to determining the location and severity of potential hazards. 3D printed models can help convey information about volcanic hazards and have advantages over traditional 2-dimensional maps. First, people can often pinpoint their location and specific geographic features more easily on a physical model, than on a map. Second, physical volcano models are effective tools for communicating risk, because volcanic hazards, such as ash flows, landslides, and lahars are so closely related to the shape of the terrain. Steep slopes are prone to failure while drainages easily become conduits for landslides and other volcanic debris. In addition, by overlaying hazard maps on top of the terrain data, models can be 3D printed in multi-color or full-color with the hazard information built right in. USGS digital elevation models (DEMs) are sufficient data sets to produce high-quality 3D models of entire volcanoes, although LIDAR may be needed to print smaller scale features. Once the data is acquired, building a 3D printable model is relatively easy.

3D printed volcano models have recently been provided to volcanic hazard experts working in Costa Rica, El Salvador, and the Caribbean island of Montserrat. These models have been used in different ways in each location and were produced using a variety of 3D printing technologies and digital data sets. Evaluation of results from these case studies will better inform future efforts and methods of producing models. At the same time, research on human perception of topographic features can help us develop a set of best practices for optimal processing of the digital data. In some cases, fine detail in high resolution DEMs can actually distract from more relevant larger order features and filtering of DEM data to highlight certain features and suppress others may improve the educational value of these 3D printed models.