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

Marijan Šapina

doi:10.17794/rgn.2016.3.6

Abstract

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.

Highlights

The main objective of this paper is to prove that artificial neural networks (ANN) can be used for the mapping of any geological variable as successfully as geostatistical interpolation methods
I confirmed the hypothesis of successful mapping using ANN
The mapping is based on depth mapping of the e-log marker “Rs5” in the Bjelovar Subdepression, as part of the Drava Depression in the Croatian part of the Pannonian Basin System

Summary

Introduction

The main objective of this paper is to prove that artificial neural networks (ANN) can be used for the mapping of any geological variable (in this case depth) as successfully as geostatistical interpolation methods. Since the data can be flawed or in a non-linear relationship, ANN can unite the data into one complex dataset The characteristic of this method is that it simulates the learning process of human beings by training and optimizing parameters in a number of repetitions. The e-log marker “Rs5” represents the border of the Moslavačka Gora Formation (Lower, Middle Miocene and Lower Pannonian sediments) and the Ivanić-Grad Formation (Upper Pannonian sediments), deposited inside the Bjelovar Subdepression, i.e. southwest of the Drava Depression (see Figure 1). This border had been previously regionally mapped by several authors A Comparison of Artificial Neural Networks and Ordinary Kriging depth maps

Basic geography and geology of the mapped area

Mapping of the e-log marker “Rs5” using Artificial Neural Networks

Variogram analysis

Interpolation using Ordinary Kriging

Findings

Discussion and conclusion