USGS seeks analytics solutions to support information analysis, tracking, point analysis, and other research and decision support requirements on challenges such as alternate use in energy and mineral resource assessments, development and analysis of research questions, modeling, and decision support application development. Specific use cases may include: support BIL funding; abandoned/orphaned wells questions support; mining sites to under mining weights questions (economic viability, resource characterization ie. mining ops via remote sensing); and understand greenhouse gasses impacts. USGS seeks to explore moving to data products and services to support continuous monitoring, which meets protocol standards and away from currently produced and processed data to support the generation of static products. 

Problem and Solution

USGS required help in both the proverbial supply and delivery chain of USGS data. To do so, Xentity provided the automation and orchestration of existing python libraries which access USGS data systems on known data models. Also, Xentity supported necessary data transformations for tabular, geospatial and raster data on improved data forms. Furthermore, workflows supported delivering integrated data into spatiotemporal data models. The workflows were generated in a new standard data form. This included usable metadata. APIs were also developed to support external calls. Finally, Xentity created demonstration-level, exemplary working interfaces and application use of resulting data, via API to support knowledge transfer.

To accomplish this, we partook in several tasks. We designed cloud-based architectural plans. Also, Xentity developed infrastructure as code. Furthermore, we implemented operational data systems on the USGS cloud. Finally, Xentity partook in a knowledge transfer and provided a sustainability plan.

Outcome and Benefit

Each of our efforts resulted in a Minimum Viable Product of an Operational framework and working data processing system in the cloud for a Spatiotemporal Feature Registry operating continuously against diverse source data (provided via one or more repositories) to produce dynamic data products for abandoned oil and gas wells, abandoned mine sites and mine waste locations, and other geographic features of interest, each with  specific combinations of source and derived attributes.

Consequently, this reduced the data compilation time for scientists, moved to making data regularly available via a continuous data pipeline, and made data available in many formats. It also achieved making data spatially available over versions of time to support temporospatial querying and analysis.