Investigation of Bingöl Iron Ore Reserves Using Wavelet Cellular Neural Networks

Abstract

In the current study, the performance of a synthetic wavelet CNN (Wave-CNN) was tested and then compared with geophysical field data; two separate synthetic studies were conducted for this purpose. In the first synthetic application, the success of the Wave-CNN method for the separation of regional-residual dipole structures was tested. In the second synthetic application, using prismatic structures with magnetic properties, the success of the Wave-CNN method to determine boundaries was compared with classical methods. It was found that the Wave-CNN method could not be repeated as many times as required. At first, it was possible to distinguish between regional and residual anomalies and successfully determine boundaries. As the number of repetitions increased, it was possible to clarify structures with stronger magnetic properties and to filter out structures with weak magnetic properties. The Wave-CNN using a vertical component of the magnetic field method was then applied to magnetic anomaly data from three Avnik iron mines located in the Bingöl Province, East Anatolia, Turkey: these areas are called, from south-to-north, Gonactepe, Heylandere and Miskel. The Wave-CNN outputs were also compared to drilling results from the study areas. Using this method, Wave-CNNs can be used to solve geophysical problems by detecting surface layer boundaries. In this Avnik field application, the Wave-CNN results showed high anomaly values indicating probable an iron ore deposit. In addition, represantive anomaly values ​​were selected from the Wave-CNN outputs of each subarea and forward modeling was performed by applying Differential Evolution (DE) method. Thus, it has been shown that a good relation is obtained with the geological cross-section and geophysical model structures.