4.5 Article

Investigation of Magneto-/Radio-Metric Behavior in Order to Identify an Estimator Model Using K-Means Clustering and Artificial Neural Network (ANN) (Iron Ore Deposit, Yazd, IRAN)

Journal

MINERALS
Volume 11, Issue 12, Pages -

Publisher

MDPI
DOI: 10.3390/min11121304

Keywords

radiometry; magnetometry; iron; k-means clustering method; artificial neural network; GRNN; BPNN

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The study area near Toot village in Yazd province, Iran, focused on iron mineralization potential. Through radiometric and magnetometric surveys, the complex relationship between magnetic anomalies and radioactivity of radioactive elements was explored. The study revealed a mathematical relationship between the Earth's magnetic field strength and radioactive elements intensity, suggesting a potential generalization of magnetometric survey results to radiometry without re-sampling.
The study area is located near Toot village in the Yazd province of Iran, which is considered in terms of its iron mineralization potential. In this area, due to radioactivity, radiometric surveys were performed in a part of the area where magnetometric studies have also been performed. According to geological studies, the presence of magnetic anomalies can have a complex relationship with the intensity of radioactivity of radioactive elements. Using the K-means clustering method, the centers of the clusters were calculated with and without considering the coordinates of radiometric points. Finally, the behavior of the two variables of magnetic field strength and radioactivity of radioactive elements relative to each other was studied, and a mathematical relationship was presented to analyze the behavior of these two variables relative to each other. On the other hand, the increasing and then decreasing behavior of the intensity of the Earth's magnetic field relative to the intensity of radioactivity of radioactive elements shows that it is possible to generalize the results of magnetometric surveys to radiometry without radiometric re-sampling in this region and neighboring areas. For this purpose, using the general regression neural network and backpropagation neural network (BPNN) methods, radiometric data were estimated with very good accuracy. The general regression neural network (GRNN) method, with more precision in estimation, was used as a model for estimating the radiation intensity of radioactive elements in other neighboring areas.

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