Airborne EM Data+ InSAR Data = Groundwater Model
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.
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.