Semantic Data And Visualization Techniques Applied To Geologic Field Mapping

Abstract:

Geologic field mapping involves the use of technology before, during, and after visiting a site. Geologists utilize hardware such as Global Positioning Systems (GPS) connected to mobile computing platforms such as tablets that include software such as ESRI’s ArcPad and other software to produce maps and figures for a final analysis and report. Hand written field notes contain important information and drawings or sketches of specific areas within the field study. Our goal is to collect and geo-tag final and raw field data into a cyber-infrastructure environment with an ontology that allows for large data processing, visualization, sharing, and searching, aiding in connecting field research with prior research in the same area and/or aid with experiment replication. Online searches of a specific field area return results such as weather data from NOAA and QuakeML seismic data from USGS. These results that can then be saved to a field mobile device and searched while in the field where there is no Internet connection. To accomplish this we created the GeoField ontology service using the Web Ontology Language (OWL) and Protégé software. Advanced queries on the dataset can be made using reasoning capabilities can be supported that go beyond a standard database service. These improvements include the automated discovery of data relevant to a specific field site and visualization techniques aimed at enhancing analysis and collaboration while in the field by draping data over mobile views of the site using augmented reality. A case study is being performed at University of Texas at El Paso's Indio Mountains Research Station located near Van Horn, Texas, an active multi-disciplinary field study site. The user can interactively move the camera around the study site and view their data digitally. Geologist’s can check their data against the site in real-time and improve collaboration with another person as both parties have the same interactive view of the data.