Victor Evgenievich Kosarev (Kazan Federal University)
Ekaterina Anatolievna Yachmenyova (Kazan Federal University)
Aleksandr Vladimirovich Starovoitov(Kazan Federal University)
Dmitrii Ivanovich Kirgizov (TNG-Group)
Rustem Ramilevich Mukhamadiev (TNG-Group)
Vladislav Anatolievich Sudakov (Kazan Federal University)
Bulat Feliksovich Akhmetov (TNG-Group)
This paper presents the efficiency of using artificial neural networks for solving problems of processing and interpreting geophysical data obtained by scanning magnetic introscopy. Neural networks of various architectures have been implemented to solve the problems of processing primary material, searching for well structure objects, identifying casing defects. The analysis of the capabilities of neural networks in comparison with mathematical algorithms is carried out. To test machine learning algorithms and mathematical algorithms for processing, visualizing and storing the results, a software shell was created in which all tasks are solved using a set of tools. It was found that the use of artificial neural networks can significantly speed up the process of data processing and interpretation, as well as improve the quality of the results in comparison with individual mathematical algorithms. Nevertheless, the use of mathematical algorithms in solving some problems gives consistently better results. In particular, the problematic aspects were identified at the stage of interpretation when identifying defects. This is due to the presence of conventions in the isolation of defects by the operator at the stage of preparing data for training neural networks, which is a subjective factor and requires a deeper study.