Click on “Download PDF” for the PDF version or on the title for the HTML version.

If you are not an ASABE member or if your employer has not arranged for access to the full-text, Click here for options.

Investigation of Subsurface Hydraulic Parameter Estimation using Airborne Electromagnetic Surveys

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  2010 Pittsburgh, Pennsylvania, June 20 - June 23, 2010  1009482.(doi:10.13031/2013.29917)
Authors:   Peter August Calvin, Debasmita Misra
Keywords:   Subsurface hydraulic characterization, Geophysical analog, Artificial Neural Network, Supervised Learning, Alaska

The characterization of subsurface hydraulic properties is critical for engineering designs in hydro-geologically and environmentally sensitive regions. Traditionally such characterization has been accomplished through drilling and well tests that measure local hydraulic and geologic properties. Due to the intrinsic variability of the subsurface hydraulics, large cross sections of samples are required to characterize a region. Such collection methods can be cost prohibitive, time inefficient, and environmentally invasive. The problematic issues related to the field collection of samples can result in a limited data set in which a large amount of interpolation and estimation is required. Airborne electromagnetic surveys (AES) offer an alternative by providing high density data coverage helping account for large spatial variability. While relationships between electrical and hydraulic properties of geologic media have been shown, methods are needed that integrate limited hydraulic data sets with geophysical surveys, such as the AES, to model the spatial variability on a large scale. In this paper the relationship between the apparent resistivity data acquired through a helicopter electromagnetic survey (HEM) and empirically determined hydraulic conductivity and its application toward large scale hydraulic modeling is explored. A geophysical data set along the Alaska Highway corridor was analyzed with respect to a subsection of hydraulic data using a supervised learning method. The computationally derived site specific relationship was then validated using the remaining hydraulic data. The results outline a method by which AES data can be used to develop a site specific relationship to model and estimate regional subsurface hydraulic parameters.

(Download PDF)    (Export to EndNotes)