from 01.01.2022 until now
Institute of Applied Geophysics named after Academician E.K. Fedorov (Nauchnyy sotrudnik)
Moscow, Moscow, Russian Federation
from 01.01.2021 until now
Moskva, Moscow, Russian Federation
VAK Russia 1.6
UDC 550.8.05
UDC 004.89
UDC 55
UDC 550.34
UDC 550.383
CSCSTI 37.01
CSCSTI 37.15
CSCSTI 37.25
CSCSTI 37.31
CSCSTI 38.01
CSCSTI 36.00
CSCSTI 37.00
CSCSTI 38.00
CSCSTI 39.00
CSCSTI 52.00
Russian Classification of Professions by Education 05.03.01
Russian Classification of Professions by Education 05.00.00
Russian Library and Bibliographic Classification 263
Russian Library and Bibliographic Classification 26
Russian Trade and Bibliographic Classification 632
Russian Trade and Bibliographic Classification 63
BISAC SCI032000 Physics / Geophysics
BISAC SCI SCIENCE
The article focuses on the application of convolutional neural networks (CNNs) for automated extraction of magnetic anomaly field lineaments. In the course of the work, an original CNN U-Net based architecture with pre-trained VGG-16 weights was developed, and its training was conducted on a sample of 500 model examples. The approach presented in this work can be an optimal tool for structural interpretation of magnetic anomaly fields. As a result of testing the proposed CNNs for magnetic field of the Barents Sea local area, the axes of the linear anomalies were identified, largely coinciding with the position of the axes obtained by manual expert interpretation. These fact demonstrates the high efficiency of applying modern artificial neural network technologies.
Convolutional Neural Networks, magnetometry, linear anomalies of the magnetic field, Barents Sea, dykes
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