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Integration of InSAR with Airborne EM Data for the Development of Groundwater Models (aka the ‘NASA Project’)

The Problem

With climate change and population growth increasing concerns about the depletion of groundwater in the western US, there is a growing need for improved tools to support sustainable groundwater-management decisions. A groundwater model is one of the key tools used for decision-making in the management of groundwater resources. Most existing models however, utilize limited geologic data at depths relevant to these management decisions. To improve the prediction accuracy of all the available models there is a critical need for data to inform the models. But the currently employed, traditional methods of acquiring data, through the drilling of wells with testing and logging, are slow, expensive and insufficient in terms of data coverage.

Our Approach

The goal of this work is to improve the spatial resolution and prediction accuracy of groundwater models by incorporating information derived from interferometric synthetic aperture radar (InSAR) data and airborne electromagnetic (AEM) data. We will develop a methodology that will update groundwater models in two locations, to obtain groundwater models with the required spatial resolution and hydrologic/geomechanical properties so as to provide improved accuracy in prediction of groundwater flow and in prediction of pumping-induced aquifer system compaction and resulting subsidence.

The Place

We will test and develop our methodology in two locations, Butte County and the Kaweah Subbasin.

 

Figure 1:  Location of the two study areas where AEM data were acquired in 2018 (Left). Zoomed in views of the two study areas, Butte and Glenn Counties (top) and the Kaweah Subbasin (bottom), showing the location of AEM flight lines in yellow

Figure 2:  Resistivity models derived from the inversion of the AEM data in a) Butte and Glenn Counties, and b) the Kaweah Subbasin. Warm colors (high resistivity) correspond to region that likely contain sands and cool colors (low resistivity) correspond to regions that likely contain clay. Management of data acquisition and processing of AEM data was done by Aqua Geo Frameworks; inversion of these data was done by Noah Dewar (Stanford University).

Results to date

AEM data have been collected in both of our study areas. Preliminary processing and inversion of these data has revealed complex and variable patterns of resistivity in the subsurface at these two sites. These resistivity models have allowed us to better understand the 3D hydrogeologic structures within each of our study areas. Further refined inversions have allowed us to investigate regions of high uncertainty in the existing groundwater models, and maximize the utility of the AEM datasets.

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Project Publications and Presentations