Abstract
Abstract. Airborne electromagnetic (AEM) methods supply data over large areas in a cost-effective way. We used Artificial Neural Networks (ANN) to classify the geophysical signal into a meaningful geological parameter. By using examples of known relations between ground-based geophysical data (in this case electrical conductivity, EC, from electrical cone penetration tests) and geological parameters (presence of glacial till), we extracted learning rules that could be applied to map the presence of a glacial till using the EC profiles from the airborne EM data. The saline groundwater in the area was obscuring the EC signal from the till but by using ANN we were able to extract subtle and often non-linear, relations in EC that were representative of the presence of the till. The ANN results were interpreted as the probability of having till and showed a good agreement with drilling data. The glacial till is acting as a layer that inhibits groundwater flow, due to its high clay-content, and is therefore an important layer in hydrogeological modelling and for predicting the effects of climate change on groundwater quantity and quality.
Highlights
Management of surface water and groundwater in deltaic areas is of paramount importance to sustain the current land use and to protect the inhabitants from flooding, either by the sea, rivers or groundwater (CLIWAT, 2011)
In this paper we describe techniques to combine 1-D data and quasi-3-D proxy data to produce a geological model of the subsurface that can be used for hydrogeological modelling
This study shows that the glacial till is an important aquitard, inhibiting groundwater flow and an important spatial feature that should be modelled as accurately as possible
Summary
Management of surface water and groundwater in deltaic areas is of paramount importance to sustain the current land use and to protect the inhabitants from flooding, either by the sea, rivers or groundwater (CLIWAT, 2011). Climate change predictions of increasing seawater levels and precipitation (especially in wintertime) will likely affect the groundwater flow and groundwater quality, especially the salinisation of groundwater (de Louw et al, 2011). To be able to forecast the effects of climate change, spatially distributed groundwater flow models (both quantity and quality) are needed. These models require input parameters (hydraulic conductivity, porosity etc.) that vary in 3-D space, and are based on the geological history of the modelled area. Examples are the use of ANN in classifying lithology (Bhattacharya and Solamatine, 2006), improving parameter extraction from geophysical data for hydrological modelling (Hinnel et al, 2010) and deriving parameters from Remote Sensing data (Krasnapolsky and Schiller, 2003). We will only discuss the basics of ANN here; the interested reader is referred to standard textbooks like Hsieh (2009)
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