A Comparison of Artificial Neural Networks and Ordinary Kriging depth maps of the Lower and Upper Pannonian stage border in the Bjelovar Subdepression, Northern Croatia
DOI:
https://doi.org/10.17794/rgn.2016.3.6Abstract
Computerised mapping of subsurface strata is possible with a wide range of methods and techniques, such as geostatistical interpolation and stochastic simulations, but also with geomathematical methods. Geomathematical methods are, for example, the use of statistics in geology and the use of artificial neural networks. Artificial neural networks are primarily used in the case of flawed data and data that is in a non-linear relation. The set hypothesis of successful mapping of depth data using this original artificial neural network algorithm is confirmed using statistical analysis and comparison with geostatistical interpolation methods. The algorithm is made in „R“, an open source statistical computing software, and is used on the mapping of depth of the e-log marker „Rs5“ in the Bjelovar Subdepression, Northern Croatia, that is the border between the Lower and Upper Pannonian stages in the Croatian part of the Pannonian Basin System. The neural network architecture that produced the best responses is a network with two hidden layers, with 10 and 6 neurons, respectively. A backpropagation algorithm is used. Two methods were compared by cross-validation and the neural network produced a mean squared error as 16294.5, and Ordinary Kriging produced 14638.35.
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