Combining ground-based and airborne em through Artificial Neural Networks for modelling glacial till under saline groundwater conditions

J. L. Gunnink, J. H.A. Bosch, B. Siemon, B. Roth, E. Auken

Publikation: Bidrag til tidsskriftArtikelForskningpeer review

42 Citationer (Scopus)

Abstrakt

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.

OriginalsprogEngelsk
Sider (fra-til)3061-3074
Antal sider14
TidsskriftHydrology and Earth System Sciences
Vol/bind16
Udgave nummer8
DOI
StatusUdgivet - 2012
Udgivet eksterntJa

Programområde

  • Programområde 2: Vandressourcer

Fingeraftryk

Dyk ned i forskningsemnerne om 'Combining ground-based and airborne em through Artificial Neural Networks for modelling glacial till under saline groundwater conditions'. Sammen danner de et unikt fingeraftryk.

Citationsformater