Potential anomaly separation using genetically trained multi-level cellular neural networks
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CitationBİLGİLİ, E., UÇAN, O.N., ALBORA, A.M., GÖKNAR, İ.C. (2002). Potential anomaly separation using genetically trained multi-level cellular neural networks. Proceedings of the 2002 7th IEEE International Workshop on Cellular Neural Networks and Their Applications, 2002 (CNNA 2002). pp.391,398. http://dx.doi.org/10.1109/CNNA.2002.1035075
In this paper, multi-level genetic cellular neural networks (ML-GCNN) are applied to the geophysical problem of potential anomaly separation and satisfactory results are obtained, compared to classical deterministic approaches. ML-GCNN is a stochastic image processing technique which is based on template optimisation using neighbourhood relationships of the pixels. The residual anomaly separation used in location decisions is one of the main problems in geophysics. The method proposed here is used in evaluating the Dumluca iron ore region of Turkey.