Global Multi-Resolution Topography (GMRT) - Making bathymetry data openly accessible
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The Global Multi-Resolution Topography (GMRT) project aims to synthesize and provide broad access to marine data, specifically using multibeam echosounder (MBES) data to create dynamic, multi-resolution topographic images and grids. Originating from the Ridge Multibeam Synthesis Project in 1992, GMRT has evolved to include various contributions from international sources, ensuring quality and accessibility across disciplines. The project focuses on enhancing data reproducibility and compliance with federal policies, offering a wide array of tools and platforms for users including researchers, educators, and citizen scientists.
Global Multi-Resolution Topography (GMRT) - Making bathymetry data openly accessible
- 1. Global Multi-Resolution Topography (GMRT) Synthesis Vicki Ferrini, Suzanne Carbotte, William Ryan, Suzanne O’Hara Lamont-Doherty Earth Observatory Columbia University
- 3. Increasing Emphasis on Open Data Access • • • • • • Acquisition Costs Spatial & Temporal Change Scientific Reproducibility Federal Data Policy Compliance New Possibilities Big Data
- 5. MBES Data Available via NGDC
- 6. How can we make data accessible to non-specialists? ?? Swath files & grids Broad access to content for: • Interdisciplinary research • Citizen scientists • Educators
- 7. GMRT Synthesis - Dynamically maintained - Multi-resolution - Images, grids, mask - 9 resolution levels to 100 m* - Full attribution to sources & contributing scientists - Source Data Includes: - ASTER, NED, GEBCO_08, IBCAO, BEDMAP *50 -Contributed grids m in places
- 8. GMRT Overview Global coverage 100m swath data and grids User experiences seamless transition between resolutions based on chosen zoom level
- 9. GMRT History • 1992 – Ridge Multibeam Synthesis Project • 2003 – Expanded to include US-funded data from Southern Ocean • 2004 – present -Expanded to include public domain data from throughout global oceans http://gmrt.marine-geo.org GMRTv2.4 - 687cruises (April 2013) ongoing growth by ~ 80 cruises/yr
- 10. GMRT Components Global & Regional Grids (>= 500 m res.) e.g. GEBCO_08, IBCAO Contributed Grids (< 500 m res.) LDEO 100-m compilation* (raw & processed swath files in public domain) MB files metadata *LDEO team performs QC of ping files
- 11. GMRT Evolution – MBES Content + 44 regional contributed grids from international sources
- 12. GMRT v1.x
- 13. GMRT v2.4
- 14. MBES Source Data in GMRTv2.4 • Kongsberg** • EM120, EM122, EM300, EM302, EM1002, EM3000 • SeaBeam • Classic, 2000, 2100, 2101, 2102, 2112 • Hydrosweep • DS, DS2 **Majority of ships US Academic Research Fleet now carry Kongsberg MBES
- 15. # cruises MBES Source Data in GMRT v2.4 year of cruise
- 16. GMRT: MB Data Preparation • • • • • • • • • Bad navigation Noisy outer beams Attitude problems Bad soundings Instrument problems Bad weather Sound velocity Slow speed in turns Quality assessment for grid weighting and resolution
- 17. GMRT MB Prep Workflow MB files Preprocess & Auto-QA Problems Manual assessment Reprocess Address Problems *MB System* Problems No problems Approval Phase Approved Add to tile set
- 18. Tools for Quality Assessment *Interface can be used to QA/QC swath data & gridded data
- 19. Tools for Quality Assessment
- 20. GMRT: Access • Images & gridded data • Desktop Apps • GeoMapApp • Virtual Ocean • Web App • CM&G • Web Map Service • Images & Mask • iPad/iPhone • Earth Observer
- 23. GMRT: Attribution & Source Data Access
- 25. Complementary Fleet-Wide Efforts GOAL: Well-documented high-quality publicly available data MAC 2011 R2R 2009 GMRT 1992
- 26. MBES Raw Data Preservation • • • • 680 file sets 253,681 files ~ 6.1 TB from 601 cruises as of Apr 15, 2013
- 28. To Learn More about GMRT… Ryan et al., 2009 http://gmrt.marine-geo.org
Editor's Notes
- #2: Seafloor bathy data are needed for a broad spectrum of studies. But coverage is exceedingly sparse and all multibeam are of high value
- #8: Seafloor bathy data are needed for a broad spectrum of studies. But coverage is exceedingly sparse and all multibeam are of high value
- #23: Through geomapapp the original data source can be identified. Here by clicking on the Italian data set Bouvet-Ligi, the location of the survey is highlighted in enclosed box on map view