Autonomous cloud based site monitoring through hydro geophysical data assimilation, processing and result delivery
Abstract
Understanding of processes in the shallow subsurface requires that geophysical, biogeochemical, hydrological and remote sensing datasets are assimilated, processed and interpreted. Multiple enabling software capabilities for process understanding have been developed by the science community. These include information models (ODM2), reactive transport modeling (PFLOTRAN, Modflow, CLM, Landlab), geophysical inversion (E4D, BERT), parameter estimation (PEST, DAKOTA), visualization (ViSiT, Paraview, D3, QGIS) as well as numerous tools written in python and R for petrophysical mapping, stochastic modeling, data analysis and so on. These capabilities use data collected using sensors and analytical tools developed by multiple manufacturers which produce many different measurements. While scientists obviously leverage tools, capabilities and lessons learned from one site at other sites, the current approach to site characterization and monitoring is very labor intensive and does not scale well. Our objective is to be able to monitor many (hundreds - thousands) of sites. This requires that monitoring can be done in a near time, affordable, auditable and essentially autonomous manner. For this we have developed a modular vertically integrated cloud based software framework which was designed from the ground up for effective site and process monitoring. This software framework (PAF - Predictive Assimilation Framework) is multitenant software and provides automation of data ingestion, processing and visualization of hydrological, geochemical and geophysical (ERT/DTS) data. The core organizational element of PAF is a project/user one in which capabilities available to users are controlled by a combination of available data and access permissions. All PAF capabilities are exposed through APIs, making it easy to quickly add new components. PAF is fully integrated with newly developed autonomous electrical geophysical hardware and thus allows for automation of electrical geophysical ingestion and processing and the ability for co analysis and visualization of the raw and processed data with other data of interest (e.g. soil temperature, soil moisture, precipitation). We will demonstrate current PAF capabilities and discuss future efforts.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2017
- Bibcode:
- 2017AGUFM.H11I1329V
- Keywords:
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- 1817 Extreme events;
- HYDROLOGY;
- 1834 Human impacts;
- HYDROLOGY;
- 1880 Water management;
- HYDROLOGY;
- 1996 Web Services;
- INFORMATICS