Offshore Geodesy: Advancing Research and Collaboration in Seafloor Deformation 


David Schmidt, Principal Investigator, University of Washington Dept. Of Earth and Space Sciences 

John DeSanto, Post Doctoral Researcher, University of Washington Dept. Of Earth and Space Sciences 

SSEC Engineers

Don Setiawan, Senior Research Software Engineer, SSEC

The Near-trench Community Geodetic Experiment, informally known as Offshore Geodesy, is a five-year NSF-funded project aimed at establishing open and accessible seafloor deformation data in the Alaska and Cascadia regions, both notorious for witnessing some of the largest earthquakes and tsunamis in recorded history. By utilizing the nascent Seafloor Geodetic Instrument Pool (SGIP), the project aims to support twelve geodetic sites on the seafloor capable of observing long-term horizontal and short-term vertical deformations in subduction tectonic environments, where understanding has been limited to current land-based tools. 

One priority of the project is to develop an open-source data processing package for the research community that will make the data more accessible to a new generation of earth scientists. To realize these goals and streamline usage of the above data, SSEC has engaged in the redevelopment of legacy FORTRAN77 code into Python3, using an extensible plug-in framework. This transition ensures sustainability, reusability, and the extensibility of both software and workflows, benefiting the broader scientific community.  

By porting the project to a more modern language, SSEC will lay the foundation for extended capabilities, improvements, and the establishment of a vibrant seafloor geodesy community. Such progress aims to foster collaboration among teams from Japan to Canada, ensuring collective progress in assessing seismic and tsunami hazards. The impact of these initiatives is substantial, preserving intellectual and technological investments made over the past three decades in understanding oceanic plate kinematics. 

Recently, the adoption of Numba, a just-in-time compiler for Python resulted in a remarkable 30% speedup in processing. Additionally, the data format has been modernized from text to .csv, enabling interoperability within the community. Additional details regarding the scientific and workforce development goals are available on the project page.