Separation of Bouguer anomaly map using cellular neural network

In this paper, a modern image-processing technique, the Cellular Neural Net work (CNN) has been firstly applied to Bouguer anomaly map of synthetic exa mples and then to data from the Sivas-Divrigi Akdag region. CNN is an analo g parallel computing paradigm defined in space and characterized by the loc ality of connections between processing neurons. The behaviour of the CNN i s defined by two template matrices and a template vector. We have optimised the weight coefficients of these templates using the Recurrent Perceptron Learning Algorithm (RPLA). After testing CNN performance on synthetic examp les, the CNN approach has been applied to the Bouguer anomaly of Sivas-Divr igi Akdag region and the results match drilling logs done by Mineral Resear ch and Exploration (MTA). (C) 2001 Published by Elsevier Science B.V.