Estimating the Depth to the Water Table from Airborne Electromagnetic Data.

Estimating the depth to the water table (DWT) from airborne electromagnetic (AEM) data is extremely challenging due to the non-unique relationship between electrical resistivity, lithology, water chemistry, and saturation. However, the distribution of the resistivity values above and below the water table will change as pore fluid influences the bulk resistivity measurement. In a single 1D resistivity model, obtained via inversion from the AEM data, this shift in resistivity is too small to be detected. However, if a sufficient number of nearby resistivity models are grouped together, the shift in the distribution should be detectable. Alternatively, if the resistivity values can be grouped by lithology into separate resistivity distributions through the application of a rock physics transform, the shift in each distribution's resistivity values should be easier to detect.

Distributions of resistivity values were constructed from groups of 1D resistivity models from an AEM survey conducted in Tulare County in the fall of 2015. The groups were built using resistivity models within a variable window distance from each other ranging from 500 to 10000 meters. Resistivity models were corrected for elevation and interpolated vertically onto uniform 2 meter thick layers. Within each group, a distribution of resistivity values was constructed for each layer and characterized using various measures to capture the range and breadth of the distribution. The rate of change in these measures was calculated and the depth at which the largest change in each was observed was taken as an estimated DWT. The best estimate for the DWT was determined using the minimum value of the resistivity distribution. The estimated DWT varied between 27 and 68 meters below the surface and when compared to DWT measurements collected by the Department of Water Resources in fall 2015 the median absolute deviation and the root mean squared deviation were 7.1 and 8.9 meters respectively. Given the spatial coverage that can be achieved with the AEM method, the ability to map the depth to the water with AEM data would be significant contribution to groundwater science and management.